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4800 papers:

POPLPOPL-2020-BeckettGMW #abstract interpretation #distributed
Abstract interpretation of distributed network control planes (RB, AG, RM, DW), p. 27.
ASPLOSASPLOS-2020-TorkMS #architecture #named
Lynx: A SmartNIC-driven Accelerator-centric Architecture for Network Servers (MT, LM, MS), pp. 117–131.
CCCC-2020-KimLKS #robust
Robust quantization of deep neural networks (YK, JL, YK, JS), pp. 74–84.
CSLCSL-2020-BielousK #game studies
Coverage and Vacuity in Network Formation Games (GB, OK), p. 18.
EDMEDM-2019-HuR #estimation #graph #performance
Academic Performance Estimation with Attention-based Graph Convolutional Networks (QH, HR).
EDMEDM-2019-KaserS #classification #interactive #modelling #student
Exploring Neural Network Models for the Classification of Students in Highly Interactive Environments (TK, DLS).
EDMEDM-2019-TatoND #hybrid #predict #reasoning
Hybrid Deep Neural Networks to Predict Socio-Moral Reasoning Skills (AANT, RN, AD).
ICSMEICSME-2019-BraiekK #approach #named #search-based #testing
DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks (HBB, FK), pp. 454–458.
ICSMEICSME-2019-PalacioMMBPS #identification #learning #using
Learning to Identify Security-Related Issues Using Convolutional Neural Networks (DNP, DM, KM, CBC, DP, CS), pp. 140–144.
MSRMSR-2019-GoteSS #git #mining #named #repository #scalability
git2net: mining time-stamped co-editing networks from large git repositories (CG, IS, FS), pp. 433–444.
MSRMSR-2019-LiuLZFDQ #commit #generative #using
Generating commit messages from diffs using pointer-generator network (QL, ZL, HZ, HF, BD, YQ), pp. 299–309.
SANERSANER-2019-NghiYJ #algorithm #classification #dependence
Bilateral Dependency Neural Networks for Cross-Language Algorithm Classification (BDQN, YY, LJ), pp. 422–433.
SANERSANER-2019-ZhangLML #approach #effectiveness #fault #named #using
CNN-FL: An Effective Approach for Localizing Faults using Convolutional Neural Networks (ZZ0, YL, XM, PL), pp. 445–455.
FMFM-2019-SuP0 #scalability
Controlling Large Boolean Networks with Temporary and Permanent Perturbations (CS, SP, JP0), pp. 707–724.
FMFM-2019-TranLMYNXJ #analysis #reachability
Star-Based Reachability Analysis of Deep Neural Networks (HDT, DML, PM, XY, LVN, WX, TTJ), pp. 670–686.
AIIDEAIIDE-2019-BontragerKASST #game studies #learning
“Superstition” in the Network: Deep Reinforcement Learning Plays Deceptive Games (PB, AK, DA, MS, CS, JT), pp. 10–16.
CoGCoG-2019-GiacomelloLL #generative
Searching the Latent Space of a Generative Adversarial Network to Generate DOOM Levels (EG, PLL, DL), pp. 1–8.
CoGCoG-2019-ParakhCS #approach #design #game studies #problem #towards
An Approach Towards Designing Problem Networks in Serious Games (AP, PC, MS), pp. 1–8.
CoGCoG-2019-ParkMMBWL #education #game studies #generative #multi
Generating Educational Game Levels with Multistep Deep Convolutional Generative Adversarial Networks (KP, BWM, WM, KEB, ENW, JCL), pp. 1–8.
CoGCoG-2019-Sovrano #experience #random
Combining Experience Replay with Exploration by Random Network Distillation (FS), pp. 1–8.
CoGCoG-2019-TongLL #evolution #policy
Enhancing Rolling Horizon Evolution with Policy and Value Networks (XT, WL, BL0), pp. 1–8.
FDGFDG-2019-HoC #community #visualisation
Roguelike ancestry network visualisation: insights from the roguelike community (XH, MC), p. 9.
FDGFDG-2019-LuoGR #parsing #using #video
Making CNNs for video parsing accessible: event extraction from DOTA2 gameplay video using transfer, zero-shot, and network pruning (ZL, MG, MR), p. 10.
FDGFDG-2019-MillerWK #evolution
Evolving unsupervised neural networks for Slither.io (MM, MW, FK), p. 5.
FDGFDG-2019-SongW #evolution #generative #named
TownSim: agent-based city evolution for naturalistic road network generation (AS, JW), p. 9.
CoGVS-Games-2019-BakalosRDDPV #identification #using
Choreographic Pose Identification using Convolutional Neural Networks (NB, IR, ND, AD, EP, AV), pp. 1–7.
CIKMCIKM-2019-ArianAAKSS #feature model #predict
Feature Enhancement via User Similarities Networks for Improved Click Prediction in Yahoo Gemini Native (MA, EA, MA, YK, OS, RS), pp. 2557–2565.
CIKMCIKM-2019-BaiYK0LY #graph #predict
Spatio-Temporal Graph Convolutional and Recurrent Networks for Citywide Passenger Demand Prediction (LB, LY, SSK, XW0, WL0, ZY), pp. 2293–2296.
CIKMCIKM-2019-BonchiGGOR #community
Discovering Polarized Communities in Signed Networks (FB, EG, AG, BO, GR), pp. 961–970.
CIKMCIKM-2019-CaiYZR
Adversarial Structured Neural Network Pruning (XC, JY, FZ, SR), pp. 2433–2436.
CIKMCIKM-2019-CaoCL0
Nested Relation Extraction with Iterative Neural Network (YC, DC0, HL0, PL0), pp. 1001–1010.
CIKMCIKM-2019-CaoDGMT #named #query
BeLink: Querying Networks of Facts, Statements and Beliefs (TDC, LD, FG, IM, XT), pp. 2941–2944.
CIKMCIKM-2019-CaoZSX #modelling #named #sequence
HiCAN: Hierarchical Convolutional Attention Network for Sequence Modeling (YC, WZ, BS, CX), pp. 1723–1732.
CIKMCIKM-2019-ChenCCR #recommendation
A Dynamic Co-attention Network for Session-based Recommendation (WC, FC, HC, MdR), pp. 1461–1470.
CIKMCIKM-2019-ChenLX0 #classification #sentiment
Sentiment Commonsense Induced Sequential Neural Networks for Sentiment Classification (SC, XL, YX, LH0), pp. 1021–1030.
CIKMCIKM-2019-ChenLYZS #graph
Knowledge-aware Textual Entailment with Graph Attention Network (DC, YL, MY0, HTZ, YS), pp. 2145–2148.
CIKMCIKM-2019-ChenSHG #detection
Attention-Residual Network with CNN for Rumor Detection (YC, JS, LH, WG), pp. 1121–1130.
CIKMCIKM-2019-ChenSTCS #performance #random
Fast and Accurate Network Embeddings via Very Sparse Random Projection (HC, SFS, YT, MC, SS), pp. 399–408.
CIKMCIKM-2019-DerrJCT
Balance in Signed Bipartite Networks (TD, CJ, YC, JT), pp. 1221–1230.
CIKMCIKM-2019-DongZHSL #detection #graph #multi
Multiple Rumor Source Detection with Graph Convolutional Networks (MD, BZ, NQVH, HS, GL), pp. 569–578.
CIKMCIKM-2019-FanBSL #classification #fine-grained #prototype #scalability
Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features (MF, YB, MS, PL0), pp. 2353–2356.
CIKMCIKM-2019-FanZDCSL #approach #graph #identification #learning #novel
Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach (CF, LZ, YD, MC, YS, ZL), pp. 559–568.
CIKMCIKM-2019-FuL #estimation #named
DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation (TYF, WCL), pp. 69–78.
CIKMCIKM-2019-Gao0WL #bidirectional #interactive #recognition
Gate-based Bidirectional Interactive Decoding Network for Scene Text Recognition (YG, YC0, JW, HL), pp. 2273–2276.
CIKMCIKM-2019-GuLL #interactive #multi
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots (JCG, ZHL, QL), pp. 2321–2324.
CIKMCIKM-2019-HeSLJPP #named #random
HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding (YH, YS, JL, CJ, JP, HP), pp. 639–648.
CIKMCIKM-2019-HuangCLCHLZZW #approach #classification #multi
Hierarchical Multi-label Text Classification: An Attention-based Recurrent Network Approach (WH, EC, QL0, YC, ZH, YL, ZZ, DZ, SW), pp. 1051–1060.
CIKMCIKM-2019-HuangSZWC #learning #self
Similarity-Aware Network Embedding with Self-Paced Learning (CH0, BS, XZ, XW, NVC), pp. 2113–2116.
CIKMCIKM-2019-HuangWWT #multi #named #self
DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting (SH, DW, XW, AT), pp. 2129–2132.
CIKMCIKM-2019-HuangWZLC #classification #prototype
Deep Prototypical Networks for Imbalanced Time Series Classification under Data Scarcity (CH0, XW, XZ, SL, NVC), pp. 2141–2144.
CIKMCIKM-2019-HuangZDB
Deep Dynamic Fusion Network for Traffic Accident Forecasting (CH, CZ, PD, LB), pp. 2673–2681.
CIKMCIKM-2019-IslamMR #graph #named #predict #social #using
NActSeer: Predicting User Actions in Social Network using Graph Augmented Neural Network (MRI, SM, NR), pp. 1793–1802.
CIKMCIKM-2019-JiaoXZZ #graph #predict
Collective Link Prediction Oriented Network Embedding with Hierarchical Graph Attention (YJ, YX, JZ, YZ), pp. 419–428.
CIKMCIKM-2019-KangT #mining #named
N2N: Network Derivative Mining (JK, HT), pp. 861–870.
CIKMCIKM-2019-Lakhotia0 #algorithm #approximate #coordination #social
Approximation Algorithms for Coordinating Ad Campaigns on Social Networks (KL, DK0), pp. 339–348.
CIKMCIKM-2019-LeePY #named
BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network (SL, CP, HY), pp. 619–628.
CIKMCIKM-2019-LeeRKKKR #graph
Graph Convolutional Networks with Motif-based Attention (JBL, RAR, XK, SK, EK, AR), pp. 499–508.
CIKMCIKM-2019-LiCWZW #feature model #graph #interactive #modelling #named #predict
Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction (ZL, ZC, SW, XZ, LW0), pp. 539–548.
CIKMCIKM-2019-LiECL #clustering #identification #mobile #multi
Multi-scale Trajectory Clustering to Identify Corridors in Mobile Networks (LL, SME, CAC, CL), pp. 2253–2256.
CIKMCIKM-2019-LiGLCYN #graph #hashtag #recommendation
Long-tail Hashtag Recommendation for Micro-videos with Graph Convolutional Network (ML, TG, ML, ZC, JY, LN), pp. 509–518.
CIKMCIKM-2019-LiHLDZ #detection #named
SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks (YL, XH, JL, MD, NZ), pp. 2233–2236.
CIKMCIKM-2019-LiLWXZHKCLL #multi #recommendation
Multi-Interest Network with Dynamic Routing for Recommendation at Tmall (CL, ZL, MW, YX, HZ, PH, GK, QC, WL, DLL), pp. 2615–2623.
CIKMCIKM-2019-LinPLO #recognition #using
An Explainable Deep Fusion Network for Affect Recognition Using Physiological Signals (JL, SP, CSL, SLO), pp. 2069–2072.
CIKMCIKM-2019-LinWXLB #estimation #hybrid #using
Path Travel Time Estimation using Attribute-related Hybrid Trajectories Network (XL, YW, XX, ZL, SSB), pp. 1973–1982.
CIKMCIKM-2019-LiQLYL #detection #graph #overview
Spam Review Detection with Graph Convolutional Networks (AL, ZQ, RL, YY, DL), pp. 2703–2711.
CIKMCIKM-2019-LiuZH #graph #representation #towards
Towards Explainable Representation of Time-Evolving Graphs via Spatial-Temporal Graph Attention Networks (ZL, DZ, JH), pp. 2137–2140.
CIKMCIKM-2019-LiY0X #component
Heterogeneous Components Fusion Network for Load Forecasting of Charging Stations (KL, FY, CF0, TX), pp. 2285–2288.
CIKMCIKM-2019-LiZWHYL #multi
Multi-Hot Compact Network Embedding (CL, LZ, SW, FH, PSY, ZL), pp. 459–468.
CIKMCIKM-2019-LongWDSJL #approach #community
Hierarchical Community Structure Preserving Network Embedding: A Subspace Approach (QL, YW, LD, GS, YJ, WL), pp. 409–418.
CIKMCIKM-2019-LuoCXQ #community
Best Co-Located Community Search in Attributed Networks (JL, XC, XX, QQ), pp. 2453–2456.
CIKMCIKM-2019-LuWSYY
Temporal Network Embedding with Micro- and Macro-dynamics (YL, XW0, CS, PSY, YY), pp. 469–478.
CIKMCIKM-2019-MauryaLM #approximate #graph #performance
Fast Approximations of Betweenness Centrality with Graph Neural Networks (SKM, XL0, TM), pp. 2149–2152.
CIKMCIKM-2019-PangWZG0 #design #generative #named
NAD: Neural Network Aided Design for Textile Pattern Generation (ZP, SW, DZ, YG, GC0), pp. 2081–2084.
CIKMCIKM-2019-PanWWYZZ #matrix #predict
Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction (ZP, ZW, WW, YY, JZ, YZ), pp. 2683–2691.
CIKMCIKM-2019-ParkKZ0Y
Task-Guided Pair Embedding in Heterogeneous Network (CP, DK, QZ, JH0, HY), pp. 489–498.
CIKMCIKM-2019-PratamaZAO0 #automation #multi #streaming
Automatic Construction of Multi-layer Perceptron Network from Streaming Examples (MP, CZ, AA, YSO, WD0), pp. 1171–1180.
CIKMCIKM-2019-QiuLHY #graph #order #recommendation
Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks (RQ, JL, ZH, HY), pp. 579–588.
CIKMCIKM-2019-QiuWCZHCZB #e-commerce
Cross-domain Attention Network with Wasserstein Regularizers for E-commerce Search (MQ, BW, CC, XZ, JH0, DC, JZ, FSB), pp. 2509–2515.
CIKMCIKM-2019-ShiLLP #multi
A Multi-Scale Temporal Feature Aggregation Convolutional Neural Network for Portfolio Management (SS, JL, GL, PP), pp. 1613–1622.
CIKMCIKM-2019-ShiSLZHLZW00
Discovering Hypernymy in Text-Rich Heterogeneous Information Network by Exploiting Context Granularity (YS, JS, YL, NZ, XH, ZL, QZ, MW, MK0, JH0), pp. 599–608.
CIKMCIKM-2019-ShiY #analysis
Recent Developments of Deep Heterogeneous Information Network Analysis (CS, PSY), pp. 2973–2974.
CIKMCIKM-2019-ShiYZ #analysis
HENA 2019: The 3rd Workshop of Heterogeneous Information Network Analysis and Applications (CS, YY, JZ), pp. 2991–2992.
CIKMCIKM-2019-SongCZX #memory management #recommendation
Session-based Recommendation with Hierarchical Memory Networks (BS, YC, WZ, CX), pp. 2181–2184.
CIKMCIKM-2019-SongS0DX0T #automation #feature model #interactive #learning #named #self
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks (WS, CS, ZX0, ZD, YX, MZ0, JT), pp. 1161–1170.
CIKMCIKM-2019-TanMYYDWTYWCCY #named #predict #risk management
UA-CRNN: Uncertainty-Aware Convolutional Recurrent Neural Network for Mortality Risk Prediction (QT, AJM, MY, BY, HD, VWSW, YKT, TCFY, GLHW, JYLC, FKLC, PCY), pp. 109–118.
CIKMCIKM-2019-Wang0T00
Discerning Edge Influence for Network Embedding (YW, YY0, HT, FX0, JL0), pp. 429–438.
CIKMCIKM-2019-WangJLHMD #community #mining #sentiment #social
Emotional Contagion-Based Social Sentiment Mining in Social Networks by Introducing Network Communities (XW, DJ, ML, DH, KM, JD), pp. 1763–1772.
CIKMCIKM-2019-WangL #behaviour #learning
Spotting Terrorists by Learning Behavior-aware Heterogeneous Network Embedding (PCW, CTL), pp. 2097–2100.
CIKMCIKM-2019-WangLL #interactive #predict
Neighborhood Interaction Attention Network for Link Prediction (ZW, YL, WL), pp. 2153–2156.
CIKMCIKM-2019-WangLZHG #e-commerce #interactive #named #recommendation
QPIN: A Quantum-inspired Preference Interactive Network for E-commerce Recommendation (PW, ZL, YZ, YH, LG), pp. 2329–2332.
CIKMCIKM-2019-WangWC #multi
Multi-Turn Response Selection in Retrieval-Based Chatbots with Iterated Attentive Convolution Matching Network (HW, ZW, JC), pp. 1081–1090.
CIKMCIKM-2019-WangWLL #named
CamDrop: A New Explanation of Dropout and A Guided Regularization Method for Deep Neural Networks (HW, GW, GL, LL), pp. 1141–1149.
CIKMCIKM-2019-WuH #scalability
Scalable Manifold-Regularized Attributed Network Embedding via Maximum Mean Discrepancy (JW, JH), pp. 2101–2104.
CIKMCIKM-2019-WuPDTZD #distance #graph #learning
Long-short Distance Aggregation Networks for Positive Unlabeled Graph Learning (MW, SP, LD, IWT, XZ, BD), pp. 2157–2160.
CIKMCIKM-2019-XiaoZZXBZY #3d #multi #recognition
Multi-view Moments Embedding Network for 3D Shape Recognition (JX, YZ, PZ, KX, KB, CZ, WY), pp. 2257–2260.
CIKMCIKM-2019-XuHY #graph #learning #scalability
Scalable Causal Graph Learning through a Deep Neural Network (CX, HH, SY), pp. 1853–1862.
CIKMCIKM-2019-XuLHLX0 #e-commerce #graph #recommendation #social
Relation-Aware Graph Convolutional Networks for Agent-Initiated Social E-Commerce Recommendation (FX, JL, ZH, YL0, YX, XX0), pp. 529–538.
CIKMCIKM-2019-YanCKWM #2d #named #recommendation
CosRec: 2D Convolutional Neural Networks for Sequential Recommendation (AY, SC, WCK, MW, JJM), pp. 2173–2176.
CIKMCIKM-2019-YangGWSX0 #summary
Query-Specific Knowledge Summarization with Entity Evolutionary Networks (CY, LG, ZW, JS, JX, JH0), pp. 2121–2124.
CIKMCIKM-2019-YangWCW #graph #predict #using
Using External Knowledge for Financial Event Prediction Based on Graph Neural Networks (YY, ZW, QC, LW), pp. 2161–2164.
CIKMCIKM-2019-YaoHGH #low level
Regularizing Deep Neural Networks by Ensemble-based Low-Level Sample-Variances Method (SY, YH, LG, ZH), pp. 1111–1120.
CIKMCIKM-2019-YeWYJZXY #behaviour #graph #representation
Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks (YY, XW, JY, KJ, JZ, YX, HY), pp. 679–688.
CIKMCIKM-2019-YouVLL #multi #recommendation
Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation (DY, NV, KL, QL), pp. 1471–1480.
CIKMCIKM-2019-ZhangFYZS #framework #identification
Key Player Identification in Underground Forums over Attributed Heterogeneous Information Network Embedding Framework (YZ, YF, YY, LZ, CS), pp. 549–558.
CIKMCIKM-2019-ZhangRZYZ #named
PRNet: Outdoor Position Recovery for Heterogenous Telco Data by Deep Neural Network (YZ, WR, KZ, MY, JZ), pp. 1933–1942.
CIKMCIKM-2019-ZhaoZSL #recommendation
Motif Enhanced Recommendation over Heterogeneous Information Network (HZ, YZ, YS, DLL), pp. 2189–2192.
ECIRECIR-p1-2019-FardBW #predict
Relationship Prediction in Dynamic Heterogeneous Information Networks (AMF, EB, KW0), pp. 19–34.
ECIRECIR-p1-2019-Sanz-CruzadoC #information retrieval #modelling #recommendation #social
Information Retrieval Models for Contact Recommendation in Social Networks (JSC, PC), pp. 148–163.
ECIRECIR-p1-2019-VoB
Extracting Temporal Event Relations Based on Event Networks (DTV, EB), pp. 844–851.
ECIRECIR-p1-2019-ZhangJ #image #predict #twitter
Image Tweet Popularity Prediction with Convolutional Neural Network (YZ0, AJ), pp. 803–809.
ECIRECIR-p2-2019-ImaniVMS19a #query #using #word
Deep Neural Networks for Query Expansion Using Word Embeddings (AI, AV, AM, AS), pp. 203–210.
ICMLICML-2019-0002LA #multi
Exploring interpretable LSTM neural networks over multi-variable data (TG0, TL, NAF), pp. 2494–2504.
ICMLICML-2019-AlbuquerqueMDCF #generative #multi
Multi-objective training of Generative Adversarial Networks with multiple discriminators (IA, JM, TD, BC, THF, IM), pp. 202–211.
ICMLICML-2019-AletJVRLK #adaptation #graph #memory management
Graph Element Networks: adaptive, structured computation and memory (FA, AKJ, MBV, AR, TLP, LPK), pp. 212–222.
ICMLICML-2019-AnconaOG #algorithm #approximate #polynomial
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation (MA, , MHG), pp. 272–281.
ICMLICML-2019-AroraDHLW #analysis #fine-grained #optimisation
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks (SA, SSD, WH, ZL0, RW), pp. 322–332.
ICMLICML-2019-BeckerPGZTN
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces (PB, HP, GHWG, CZ, CJT, GN), pp. 544–552.
ICMLICML-2019-BehrmannGCDJ
Invertible Residual Networks (JB, WG, RTQC, DD, JHJ), pp. 573–582.
ICMLICML-2019-BiePC #probability
Stochastic Deep Networks (GdB, GP, MC), pp. 1556–1565.
ICMLICML-2019-BiettiMCM #kernel
A Kernel Perspective for Regularizing Deep Neural Networks (AB, GM, DC, JM), pp. 664–674.
ICMLICML-2019-CasadoM #constraints #orthogonal
Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group (MLC, DMR), pp. 3794–3803.
ICMLICML-2019-ChattopadhyayMS #perspective
Neural Network Attributions: A Causal Perspective (AC, PM, AS, VNB), pp. 981–990.
ICMLICML-2019-ChenLCZ #comprehension
Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels (PC, BL, GC, SZ), pp. 1062–1070.
ICMLICML-2019-ChenTZB00 #approach #generative
A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization (YC, MT, CZ, BB, DH0, JP0), pp. 1071–1080.
ICMLICML-2019-ChiquetRM #re-engineering
Variational Inference for sparse network reconstruction from count data (JC, SR, MM), pp. 1162–1171.
ICMLICML-2019-CohenWKW
Gauge Equivariant Convolutional Networks and the Icosahedral CNN (TC, MW, BK, MW), pp. 1321–1330.
ICMLICML-2019-DuH #linear #matter #optimisation
Width Provably Matters in Optimization for Deep Linear Neural Networks (SSD, WH), pp. 1655–1664.
ICMLICML-2019-DuLL0Z
Gradient Descent Finds Global Minima of Deep Neural Networks (SSD, JDL, HL, LW0, XZ), pp. 1675–1685.
ICMLICML-2019-DziedzicPKEF
Band-limited Training and Inference for Convolutional Neural Networks (AD, JP, SK, AJE, MJF), pp. 1745–1754.
ICMLICML-2019-FischerBDGZV #logic #named #query
DL2: Training and Querying Neural Networks with Logic (MF, MB, DDC, TG, CZ, MTV), pp. 1931–1941.
ICMLICML-2019-FranceschiNPH #graph #learning
Learning Discrete Structures for Graph Neural Networks (LF, MN, MP, XH), pp. 1972–1982.
ICMLICML-2019-FuLTL #black box #generative #metric #named #optimisation #speech
MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement (SWF, CFL, YT0, SDL), pp. 2031–2041.
ICMLICML-2019-GeifmanE #named
SelectiveNet: A Deep Neural Network with an Integrated Reject Option (YG, REY), pp. 2151–2159.
ICMLICML-2019-GhaffariLM #algorithm #clustering #parallel
Improved Parallel Algorithms for Density-Based Network Clustering (MG, SL, SM), pp. 2201–2210.
ICMLICML-2019-GoldfeldBGMNKP #data flow
Estimating Information Flow in Deep Neural Networks (ZG, EvdB, KHG, IM, NN, BK, YP), pp. 2299–2308.
ICMLICML-2019-HaberLTR
IMEXnet A Forward Stable Deep Neural Network (EH, KL, ET, LR), pp. 2525–2534.
ICMLICML-2019-HacohenW #education #learning #on the #power of
On The Power of Curriculum Learning in Training Deep Networks (GH, DW), pp. 2535–2544.
ICMLICML-2019-HaninR #complexity #linear
Complexity of Linear Regions in Deep Networks (BH, DR), pp. 2596–2604.
ICMLICML-2019-HavivRB #comprehension #memory management
Understanding and Controlling Memory in Recurrent Neural Networks (DH, AR, OB), pp. 2663–2671.
ICMLICML-2019-HayouDR #on the
On the Impact of the Activation function on Deep Neural Networks Training (SH, AD, JR), pp. 2672–2680.
ICMLICML-2019-HsiehLC #generative #nash
Finding Mixed Nash Equilibria of Generative Adversarial Networks (YPH, CL, VC), pp. 2810–2819.
ICMLICML-2019-JeongKKN #graph #modelling #music #performance
Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance (DJ, TK, YK, JN), pp. 3060–3070.
ICMLICML-2019-JeongLK
Ladder Capsule Network (TJ, YL, HK), pp. 3071–3079.
ICMLICML-2019-KayaHD #comprehension
Shallow-Deep Networks: Understanding and Mitigating Network Overthinking (YK, SH, TD), pp. 3301–3310.
ICMLICML-2019-KenterWCCV #named #speech #synthesis
CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network (TK, VW, CaC, RC, JV), pp. 3331–3340.
ICMLICML-2019-KoLWDWL #named #robust
POPQORN: Quantifying Robustness of Recurrent Neural Networks (CYK, ZL, LW, LD, NW, DL), pp. 3468–3477.
ICMLICML-2019-Kornblith0LH #revisited #similarity
Similarity of Neural Network Representations Revisited (SK, MN0, HL, GEH), pp. 3519–3529.
ICMLICML-2019-Labatie
Characterizing Well-Behaved vs. Pathological Deep Neural Networks (AL), pp. 3611–3621.
ICMLICML-2019-LambBGSMBM #modelling
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations (AL, JB, AG, SS, IM, YB, MM), pp. 3622–3631.
ICMLICML-2019-LeeLKKCT #framework #invariant #set
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks (JL, YL, JK, ARK, SC, YWT), pp. 3744–3753.
ICMLICML-2019-LiDMMHH #learning #named
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning (HYL, WD, XM, CM, FH, BGH), pp. 3825–3834.
ICMLICML-2019-LiGDVK #graph #learning #similarity
Graph Matching Networks for Learning the Similarity of Graph Structured Objects (YL, CG, TD, OV, PK), pp. 3835–3845.
ICMLICML-2019-LiLWZG #black box #learning #named
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks (YL, LL, LW, TZ, BG), pp. 3866–3876.
ICMLICML-2019-LiYZH
Feature-Critic Networks for Heterogeneous Domain Generalization (YL, YY, WZ, TMH), pp. 3915–3924.
ICMLICML-2019-Ma0KW0 #graph
Disentangled Graph Convolutional Networks (JM, PC0, KK, XW0, WZ0), pp. 4212–4221.
ICMLICML-2019-MahoneyM #modelling
Traditional and Heavy Tailed Self Regularization in Neural Network Models (MWM, CM), pp. 4284–4293.
ICMLICML-2019-MaronFSL #invariant #on the
On the Universality of Invariant Networks (HM, EF, NS, YL), pp. 4363–4371.
ICMLICML-2019-MehtaCR #graph #probability
Stochastic Blockmodels meet Graph Neural Networks (NM, LC, PR), pp. 4466–4474.
ICMLICML-2019-MellerFAG #fault
Same, Same But Different: Recovering Neural Network Quantization Error Through Weight Factorization (EM, AF, UA, MG), pp. 4486–4495.
ICMLICML-2019-MendisRAC #estimation #named #performance #throughput #using
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks (CM, AR, SPA, MC), pp. 4505–4515.
ICMLICML-2019-MostafaW #parametricity #performance
Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization (HM, XW), pp. 4646–4655.
ICMLICML-2019-NayakMSRC
Zero-Shot Knowledge Distillation in Deep Networks (GKN, KRM, VS, VBR, AC), pp. 4743–4751.
ICMLICML-2019-NoklandE #fault
Training Neural Networks with Local Error Signals (AN, LHE), pp. 4839–4850.
ICMLICML-2019-OdenaOAG #debugging #fuzzing #named
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing (AO, CO, DA, IJG), pp. 4901–4911.
ICMLICML-2019-OonoS #approximate #estimation #parametricity
Approximation and non-parametric estimation of ResNet-type convolutional neural networks (KO, TS), pp. 4922–4931.
ICMLICML-2019-PanousisCT #contest #parametricity
Nonparametric Bayesian Deep Networks with Local Competition (KPP, SC, ST), pp. 4980–4988.
ICMLICML-2019-ParkSLS #empirical #probability
The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study (DSP, JSD, QVL, SLS), pp. 5042–5051.
ICMLICML-2019-PengWCH #collaboration
Collaborative Channel Pruning for Deep Networks (HP, JW, SC, JH), pp. 5113–5122.
ICMLICML-2019-QuBT #graph #markov #named
GMNN: Graph Markov Neural Networks (MQ, YB, JT0), pp. 5241–5250.
ICMLICML-2019-RahamanBADLHBC #bias #on the
On the Spectral Bias of Neural Networks (NR, AB, DA, FD, ML, FAH, YB, ACC), pp. 5301–5310.
ICMLICML-2019-RahmanJG #compilation
Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation (TR, SJ, VG), pp. 5311–5320.
ICMLICML-2019-RatzlaffL #generative #named
HyperGAN: A Generative Model for Diverse, Performant Neural Networks (NR, FL), pp. 5361–5369.
ICMLICML-2019-ShenHCD #independence #testing
Conditional Independence in Testing Bayesian Networks (YS, HH, AC, AD), pp. 5701–5709.
ICMLICML-2019-ShiK0 #modelling #scalability
Scalable Training of Inference Networks for Gaussian-Process Models (JS, MEK, JZ0), pp. 5758–5768.
ICMLICML-2019-Simon-GabrielOB #first-order
First-Order Adversarial Vulnerability of Neural Networks and Input Dimension (CJSG, YO, LB, BS, DLP), pp. 5809–5817.
ICMLICML-2019-SimsekliSG #analysis #probability
A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks (US, LS, MG), pp. 5827–5837.
ICMLICML-2019-SinghTJGB #generative #parametricity
Non-Parametric Priors For Generative Adversarial Networks (RS, PKT, SJ, RG, MWB), pp. 5838–5847.
ICMLICML-2019-TaiBV
Equivariant Transformer Networks (KST, PB, GV), pp. 6086–6095.
ICMLICML-2019-TanL #named #scalability
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (MT, QVL), pp. 6105–6114.
ICMLICML-2019-TranKSK #named #using
DeepNose: Using artificial neural networks to represent the space of odorants (NBT, DRK, SS, AAK), pp. 6305–6314.
ICMLICML-2019-TurnerHFSY #generative
Metropolis-Hastings Generative Adversarial Networks (RDT, JH, EF, YS, JY), pp. 6345–6353.
ICMLICML-2019-VladimirovaVMA #comprehension
Understanding Priors in Bayesian Neural Networks at the Unit Level (MV, JV, PM, JA), pp. 6458–6467.
ICMLICML-2019-Wang0XZ
Convolutional Poisson Gamma Belief Network (CW, BC0, SX, MZ), pp. 6515–6525.
ICMLICML-2019-WangN
State-Regularized Recurrent Neural Networks (CW, MN), pp. 6596–6606.
ICMLICML-2019-WangZB #bias #matter
Bias Also Matters: Bias Attribution for Deep Neural Network Explanation (SW, TZ, JAB), pp. 6659–6667.
ICMLICML-2019-WangZB19a
Jumpout : Improved Dropout for Deep Neural Networks with ReLUs (SW, TZ, JAB), pp. 6668–6676.
ICMLICML-2019-WengCNSBOD #approach #named #probability #robust #verification
PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach (LW, PYC, LMN, MSS, AB, IVO, LD), pp. 6727–6736.
ICMLICML-2019-WiqvistMPF #approximate #architecture #learning #statistics #summary
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation (SW, PAM, UP, JF), pp. 6798–6807.
ICMLICML-2019-WuSZFYW #graph
Simplifying Graph Convolutional Networks (FW, AHSJ, TZ, CF, TY, KQW), pp. 6861–6871.
ICMLICML-2019-YangWLCXS0X #named #performance
LegoNet: Efficient Convolutional Neural Networks with Lego Filters (ZY, YW, CL, HC, CX, BS, CX0, CX0), pp. 7005–7014.
ICMLICML-2019-YoonSM #adaptation #learning #named
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning (SWY, JS, JM), pp. 7115–7123.
ICMLICML-2019-YouYL #graph
Position-aware Graph Neural Networks (JY, RY, JL), pp. 7134–7143.
ICMLICML-2019-YuCGY #graph #learning #named
DAG-GNN: DAG Structure Learning with Graph Neural Networks (YY, JC, TG, MY), pp. 7154–7163.
ICMLICML-2019-YurochkinAGGHK #learning #parametricity
Bayesian Nonparametric Federated Learning of Neural Networks (MY, MA, SG, KHG, TNH, YK), pp. 7252–7261.
ICMLICML-2019-YuTRKSAZL #distributed #learning
Distributed Learning over Unreliable Networks (CY, HT, CR, SK, AS, DA, CZ, JL0), pp. 7202–7212.
ICMLICML-2019-Zhang #invariant
Making Convolutional Networks Shift-Invariant Again (RZ), pp. 7324–7334.
ICMLICML-2019-ZhangGMO #generative #self
Self-Attention Generative Adversarial Networks (HZ0, IJG, DNM, AO), pp. 7354–7363.
ICMLICML-2019-ZhangHK #design #distributed #graph #named
Circuit-GNN: Graph Neural Networks for Distributed Circuit Design (GZ, HH, DK), pp. 7364–7373.
ICMLICML-2019-ZhangS #learning
Co-Representation Network for Generalized Zero-Shot Learning (FZ, GS), pp. 7434–7443.
ICMLICML-2019-ZhangZ
Interpreting Adversarially Trained Convolutional Neural Networks (TZ, ZZ), pp. 7502–7511.
ICMLICML-2019-ZhaoHDSZ #using
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting (RZ, YH, JD, CDS, ZZ), pp. 7543–7552.
ICMLICML-2019-ZhouLLLZZ #comprehension #towards
Toward Understanding the Importance of Noise in Training Neural Networks (MZ, TL, YL, DL, EZ, TZ), pp. 7594–7602.
KDDKDD-2019-0001WAT #graph
Graph Convolutional Networks with EigenPooling (YM0, SW, CCA, JT), pp. 723–731.
KDDKDD-2019-AmelkinS #recommendation #social
Fighting Opinion Control in Social Networks via Link Recommendation (VA, AKS), pp. 677–685.
KDDKDD-2019-CenZZYZ0 #learning #multi #representation
Representation Learning for Attributed Multiplex Heterogeneous Network (YC, XZ, JZ, HY, JZ, JT0), pp. 1358–1368.
KDDKDD-2019-ChenBF #modelling #on the
On Dynamic Network Models and Application to Causal Impact (YCC, ASB, JLF), pp. 1194–1204.
KDDKDD-2019-ChiangLSLBH #algorithm #clustering #graph #named #performance #scalability
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks (WLC, XL, SS, YL0, SB, CJH), pp. 257–266.
KDDKDD-2019-Do0V #graph transformation #policy #predict
Graph Transformation Policy Network for Chemical Reaction Prediction (KD, TT0, SV), pp. 750–760.
KDDKDD-2019-DongBB
Network Density of States (KD, ARB, DB), pp. 1152–1161.
KDDKDD-2019-Eliassi-RadCL #bias
Incompleteness in Networks: Biases, Skewed Results, and Some Solutions (TER, RSC, TL), pp. 3217–3218.
KDDKDD-2019-FanZHSHML #graph #recommendation
Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation (SF, JZ, XH, CS, LH, BM, YL), pp. 2478–2486.
KDDKDD-2019-FawazKPSM #quantum
Training and Meta-Training Binary Neural Networks with Quantum Computing (AF, PK, SP, SS, PM), pp. 1674–1681.
KDDKDD-2019-GaoJ #graph #learning #representation
Graph Representation Learning via Hard and Channel-Wise Attention Networks (HG, SJ), pp. 741–749.
KDDKDD-2019-GaoPH #graph #random
Conditional Random Field Enhanced Graph Convolutional Neural Networks (HG, JP, HH), pp. 276–284.
KDDKDD-2019-GaoPH19a #generative #named #proximity
ProGAN: Network Embedding via Proximity Generative Adversarial Network (HG, JP, HH), pp. 1308–1316.
KDDKDD-2019-GengLLJXZYLZ #named #predict
LightNet: A Dual Spatiotemporal Encoder Network Model for Lightning Prediction (YaG, QL, TL, LJ, LX, DZ, WY, WL, YZ), pp. 2439–2447.
KDDKDD-2019-GrislainPT #probability #realtime
Recurrent Neural Networks for Stochastic Control in Real-Time Bidding (NG, NP0, AT), pp. 2801–2809.
KDDKDD-2019-GuoHJZW0 #behaviour #multi #predict #realtime #using
Buying or Browsing?: Predicting Real-time Purchasing Intent using Attention-based Deep Network with Multiple Behavior (LG, LH, RJ, BZ, XW, BC0), pp. 1984–1992.
KDDKDD-2019-HanYZSLZ0K #graph #identification #matrix #named
GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization (PH, PY, PZ, SS, YL0, JZ, XG0, PK), pp. 705–713.
KDDKDD-2019-HartvigsenSKR #adaptation #classification #policy
Adaptive-Halting Policy Network for Early Classification (TH, CS, XK, EAR), pp. 101–110.
KDDKDD-2019-HeLLH #learning
Learning Network-to-Network Model for Content-rich Network Embedding (ZH, JL0, NL, YH), pp. 1037–1045.
KDDKDD-2019-Huang0DLLPST0Y0 #algorithm #learning #theory and practice
Learning From Networks: Algorithms, Theory, and Applications (XH, PC0, YD, JL, HL0, JP, LS, JT0, FW0, HY, WZ0), pp. 3221–3222.
KDDKDD-2019-HuangSLH #graph #random
Graph Recurrent Networks With Attributed Random Walks (XH, QS, YL, XH), pp. 732–740.
KDDKDD-2019-HuFS #learning
Adversarial Learning on Heterogeneous Information Networks (BH, YF0, CS), pp. 120–129.
KDDKDD-2019-HuH #learning #named #set
Sets2Sets: Learning from Sequential Sets with Neural Networks (HH, XH0), pp. 1491–1499.
KDDKDD-2019-JinHSWLSK #email
Smart Roles: Inferring Professional Roles in Email Networks (DJ, MH, TS, MW, WL, LS, DK), pp. 2923–2933.
KDDKDD-2019-JinRKKRK #summary
Latent Network Summarization: Bridging Network Embedding and Summarization (DJ, RAR, EK, SK, AR, DK), pp. 987–997.
KDDKDD-2019-KitadaIS #effectiveness #multi #predict #using
Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creatives (SK, HI, YS), pp. 2069–2077.
KDDKDD-2019-KumarZL #interactive #predict
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks (SK, XZ, JL), pp. 1269–1278.
KDDKDD-2019-LiGL0 #adaptation #feature model
Adaptive Unsupervised Feature Selection on Attributed Networks (JL, RG, CL, HL0), pp. 92–100.
KDDKDD-2019-LiQWM #ranking
Combining Decision Trees and Neural Networks for Learning-to-Rank in Personal Search (PL0, ZQ, XW, DM), pp. 2032–2040.
KDDKDD-2019-LiST #classification #higher-order #markov #multi #predict #random
Multi-task Recurrent Neural Networks and Higher-order Markov Random Fields for Stock Price Movement Prediction: Multi-task RNN and Higer-order MRFs for Stock Price Classification (CL, DS, DT), pp. 1141–1151.
KDDKDD-2019-LiuTLYZH
Is a Single Vector Enough?: Exploring Node Polysemy for Network Embedding (NL, QT, YL, HY, JZ, XH), pp. 932–940.
KDDKDD-2019-MaKL #recommendation
Hierarchical Gating Networks for Sequential Recommendation (CM, PK, XL), pp. 825–833.
KDDKDD-2019-MartinM #quality #statistics
Statistical Mechanics Methods for Discovering Knowledge from Modern Production Quality Neural Networks (CHM, MWM), pp. 3239–3240.
KDDKDD-2019-MengZXZX #predict
A Hierarchical Career-Path-Aware Neural Network for Job Mobility Prediction (QM, HZ, KX, LZ, HX), pp. 14–24.
KDDKDD-2019-OuyangZLZXLD #predict
Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction (WO, XZ, LL, HZ, XX, ZL, YD), pp. 2078–2086.
KDDKDD-2019-PangSH #detection
Deep Anomaly Detection with Deviation Networks (GP, CS, AvdH), pp. 353–362.
KDDKDD-2019-ParkKDZF #graph #using
Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks (NP, AK, XLD, TZ, CF), pp. 596–606.
KDDKDD-2019-RozenshteinG #mining
Mining Temporal Networks (PR, AG), pp. 3225–3226.
KDDKDD-2019-ShangSL0 #mining
Constructing and Mining Heterogeneous Information Networks from Massive Text (JS, JS, LL, JH0), pp. 3191–3192.
KDDKDD-2019-SunZZSHX #approach
The Impact of Person-Organization Fit on Talent Management: A Structure-Aware Convolutional Neural Network Approach (YS, FZ, HZ, XS, QH, HX), pp. 1625–1633.
KDDKDD-2019-SuZNLSP #detection #multi #probability #robust
Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network (YS, YZ, CN, RL, WS, DP), pp. 2828–2837.
KDDKDD-2019-TaoLZZWFC #detection #game studies #multi #named #online
MVAN: Multi-view Attention Networks for Real Money Trading Detection in Online Games (JT, JL, SZ, SZ, RW, CF, PC), pp. 2536–2546.
KDDKDD-2019-TuM0P0 #named #optimisation
AutoNE: Hyperparameter Optimization for Massive Network Embedding (KT, JM, PC0, JP, WZ0), pp. 216–225.
KDDKDD-2019-VermaZ #graph
Stability and Generalization of Graph Convolutional Neural Networks (SV, ZLZ), pp. 1539–1548.
KDDKDD-2019-Wang00LC #graph #named #recommendation
KGAT: Knowledge Graph Attention Network for Recommendation (XW, XH0, YC0, ML0, TSC), pp. 950–958.
KDDKDD-2019-WangWZPL #algorithm #personalisation #recommendation
Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation (JW, NW, WXZ, FP, XL), pp. 539–547.
KDDKDD-2019-WangXLLCDWS #framework #learning #multi #named #social
MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network (HW, TX, QL0, DL, EC, DD, HW, WS), pp. 1064–1072.
KDDKDD-2019-WangZTWX #named #using
AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks (JW, YZ0, KT, JW, ZX), pp. 1900–1908.
KDDKDD-2019-WangZZLZLW #graph #recommendation
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems (HW, FZ, MZ, JL, MZ, WL, ZW), pp. 968–977.
KDDKDD-2019-WeiCZWGXL #coordination #learning #named
PressLight: Learning Max Pressure Control to Coordinate Traffic Signals in Arterial Network (HW, CC, GZ, KW, VVG, KX, ZL), pp. 1290–1298.
KDDKDD-2019-WuHX #classification #graph #named
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification (JW, JH, JX), pp. 406–415.
KDDKDD-2019-XiaLGLCS #case study #detection #mobile #privacy #social
Characterizing and Detecting Malicious Accounts in Privacy-Centric Mobile Social Networks: A Case Study (ZX, CL, NZG, QL0, YC0, DS), pp. 2012–2022.
KDDKDD-2019-XuH0D #predict #social
Link Prediction with Signed Latent Factors in Signed Social Networks (PX, WH, JW0, BD), pp. 1046–1054.
KDDKDD-2019-Yang #framework #graph #named #platform
AliGraph: A Comprehensive Graph Neural Network Platform (HY), pp. 3165–3166.
KDDKDD-2019-YanZDSSK #classification #named
GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data (YY, JZ, MD, ES, CSS, DK), pp. 772–782.
KDDKDD-2019-YeSDFTX #multi #predict
Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network (JY, LS, BD, YF, XT, HX), pp. 305–313.
KDDKDD-2019-ZhangSHSC #graph
Heterogeneous Graph Neural Network (CZ, DS, CH0, AS, NVC), pp. 793–803.
KDDKDD-2019-ZhaoDSZLX #learning #multi #relational
Multiple Relational Attention Network for Multi-task Learning (JZ, BD, LS, FZ, WL, HX), pp. 1123–1131.
KDDKDD-2019-ZheSX #community #detection #scalability
Community Detection on Large Complex Attribute Network (CZ, AS, XX), pp. 2041–2049.
KDDKDD-2019-ZhouMGBB #comprehension #using
Understanding Consumer Journey using Attention based Recurrent Neural Networks (YZ, SM, JG, TB, NB), pp. 3102–3111.
KDDKDD-2019-ZhouMZ #memory management #personalisation #recommendation #topic
Topic-Enhanced Memory Networks for Personalised Point-of-Interest Recommendation (XZ, CM, ZZ), pp. 3018–3028.
KDDKDD-2019-ZhuZ00 #graph #robust
Robust Graph Convolutional Networks Against Adversarial Attacks (DZ, ZZ, PC0, WZ0), pp. 1399–1407.
KDDKDD-2019-ZugnerG #graph #robust
Certifiable Robustness and Robust Training for Graph Convolutional Networks (DZ, SG), pp. 246–256.
MoDELSMoDELS-2019-BurguenoCG #architecture #model transformation
An LSTM-Based Neural Network Architecture for Model Transformations (LB, JC, SG), pp. 294–299.
OOPSLAOOPSLA-2019-LiWNN #debugging #detection #learning #representation
Improving bug detection via context-based code representation learning and attention-based neural networks (YL, SW0, TNN, SVN), p. 30.
PLATEAUPLATEAU-2019-ZhaoF0I #learning #live programming #programming #visualisation
Live Programming Environment for Deep Learning with Instant and Editable Neural Network Visualization (CZ, TF, JK0, TI), p. 5.
PEPMPEPM-2019-XuZWCGX
Method name suggestion with hierarchical attention networks (SX, SZ, WW, XC, CG, JX0), pp. 10–21.
PLDIPLDI-2019-AndersonPDC #abstraction #approach #optimisation #robust
Optimization and abstraction: a synergistic approach for analyzing neural network robustness (GA, SP, ID, SC), pp. 731–744.
PLDIPLDI-2019-SmolkaKKFHK0 #probability #scalability #verification
Scalable verification of probabilistic networks (SS, PK0, DMK, NF, JH, DK, AS0), pp. 190–203.
POPLPOPL-2019-SinghGPV #abstract domain
An abstract domain for certifying neural networks (GS, TG, MP, MTV), p. 30.
SASSAS-2019-LiLYCHZ #performance #precise #towards #verification
Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verification (JL, JL, PY, LC, XH0, LZ0), pp. 296–319.
ASEASE-2019-BaoLWF #automation #generative #named
ACTGAN: Automatic Configuration Tuning for Software Systems with Generative Adversarial Networks (LB, XL, FW, BF), pp. 465–476.
ASEASE-2019-BuiYJ #named
AutoFocus: Interpreting Attention-Based Neural Networks by Code Perturbation (NDQB, YY, LJ), pp. 38–41.
ASEASE-2019-ChenPSAZ #cyber-physical #fuzzing #testing
Learning-Guided Network Fuzzing for Testing Cyber-Physical System Defences (YC, CMP, JS, SA, FZ), pp. 962–973.
ASEASE-2019-DuX000Z #analysis #framework
A Quantitative Analysis Framework for Recurrent Neural Network (XD, XX, YL0, LM0, YL0, JZ), pp. 1062–1065.
ASEASE-2019-GopinathCPT
Property Inference for Deep Neural Networks (DG, HC, CSP, AT), pp. 797–809.
ASEASE-2019-WanSSXZ0Y #learning #multi #retrieval #semantics #source code
Multi-modal Attention Network Learning for Semantic Source Code Retrieval (YW, JS, YS, GX, ZZ, JW0, PSY), pp. 13–25.
ASEASE-2019-XieCLM0Z #fuzzing
Coverage-Guided Fuzzing for Feedforward Neural Networks (XX, HC, YL0, LM0, YL0, JZ), pp. 1162–1165.
ESEC-FSEESEC-FSE-2019-Golzadeh #congruence #dependence
Analysing socio-technical congruence in the package dependency network of Cargo (MG), pp. 1226–1228.
ESEC-FSEESEC-FSE-2019-Pan #analysis #robust
Static deep neural network analysis for robustness (RP), pp. 1238–1240.
ICSE-2019-HaZ #configuration management #named #performance #predict
DeepPerf: performance prediction for configurable software with deep sparse neural network (HH, HZ0), pp. 1095–1106.
ICSE-2019-WangD00Z #detection #mutation testing #testing
Adversarial sample detection for deep neural network through model mutation testing (JW, GD, JS0, XW0, PZ), pp. 1245–1256.
ASPLOSASPLOS-2019-JinH #memory management #named #optimisation
Split-CNN: Splitting Window-based Operations in Convolutional Neural Networks for Memory System Optimization (TJ, SH), pp. 835–847.
ASPLOSASPLOS-2019-KungMZ #array #implementation #optimisation #performance
Packing Sparse Convolutional Neural Networks for Efficient Systolic Array Implementations: Column Combining Under Joint Optimization (HTK, BM, SQZ), pp. 821–834.
ASPLOSASPLOS-2019-LascorzJSPMSNSM #approach #hardware #named
Bit-Tactical: A Software/Hardware Approach to Exploiting Value and Bit Sparsity in Neural Networks (ADL, PJ, DMS, ZP, MM, SS, MN, KS, AM), pp. 749–763.
ASPLOSASPLOS-2019-RouhaniCK #framework #named
DeepSigns: An End-to-End Watermarking Framework for Ownership Protection of Deep Neural Networks (BDR, HC, FK), pp. 485–497.
CASECASE-2019-ChangLH #coordination #detection #realtime #using
Real-Time Object Coordinate Detection and Manipulator Control Using Rigidly Trained Convolutional Neural Networks (YMC, CHGL, YFH), pp. 1347–1352.
CASECASE-2019-JiangL #bound #game studies
Bayesian Stackelberg Game Model for Water Supply Networks against Bounded Rational Interdictors (JJ, XL), pp. 842–847.
CASECASE-2019-KebriaKJN19a #fuzzy #nondeterminism
Type-2 Fuzzy Neural Network Synchronization of Teleoperation Systems with Delay and Uncertainties (PMK, AK, SMJJ, SN), pp. 1625–1630.
CASECASE-2019-LiuZHZWW #estimation #learning #using
sEMG-Based Continuous Estimation of Knee Joint Angle Using Deep Learning with Convolutional Neural Network (GL, LZ, BH, TZ, ZW, PW), pp. 140–145.
CASECASE-2019-MaoDSK #optimisation
Optimizing a UAV-based Emergency Medical Service Network for Trauma Injury Patients (RM, BD, DS, NK), pp. 721–726.
CASECASE-2019-Martinez-SeisLW #community #quality #social
Measure community quality by attribute importance and density in social networks (BMS, XL, XW), pp. 628–633.
CASECASE-2019-NguyenLLGL #self #using
Visual-Guided Robot Arm Using Self-Supervised Deep Convolutional Neural Networks (VTN, CL, CHGL, SMG, JJJL), pp. 1415–1420.
CASECASE-2019-NiuLNLW #detection #fault #generative #named #using
DefectGAN: Weakly-Supervised Defect Detection using Generative Adversarial Network (SN, HL, TN, BL, XW), pp. 127–132.
CASECASE-2019-PriggemeyerGR
Passive Device Synchronization for the External Control of Robotic Applications in Ethernet Networks based on a Phase-Locked Loop (MP, KG, JR), pp. 1643–1650.
CASECASE-2019-SubramanianC #automation #embedded #normalisation
Mean Spectral Normalization of Deep Neural Networks for Embedded Automation (AKS, NYC), pp. 249–256.
CASECASE-2019-SunLY #detection #using
Railway Joint Detection Using Deep Convolutional Neural Networks (YS, YL, CY), pp. 235–240.
CASECASE-2019-SunW #algorithm #assembly #design #hybrid
Hybrid Evolutionary Algorithm for Integrated Supply Chain Network Design With Assembly Line Balancing (BqS, LW), pp. 885–890.
CASECASE-2019-TsayL #automation #multi #visual notation
Automating Visual Inspection of Lyophilized Drug Products With Multi-Input Deep Neural Networks (CT, ZL), pp. 1802–1807.
CASECASE-2019-WuPZ #algorithm #fault tolerance #heuristic
A heuristic pathfinding algorithm for dynamic fault tolerance in manufacturing networks (YW, GP, HZ), pp. 1580–1585.
CASECASE-2019-ZhangDWLL #estimation
Remaining Useful Life Estimation Based on a New Convolutional and Recurrent Neural Network (XZ, YD, LW, FL, WL), pp. 317–322.
CASECASE-2019-ZhangLWGG #fault #learning #using
Fault Diagnosis Using Unsupervised Transfer Learning Based on Adversarial Network (ZZ, XL, LW, LG0, YG), pp. 305–310.
CASECASE-2019-ZhangXZW #algorithm #distributed #estimation
A Decentralized State Estimation Algorithm for Building Electrical Distribution Network Based on ADMM (YZ, JX, QZ, SW), pp. 756–761.
CASECASE-2019-ZhaoXSLSW #3d #predict
Nonlinear Deformation Prediction and Compensation for 3D Printing Based on CAE Neural Networks (MZ, GX, XS, CL, ZS, HW), pp. 667–672.
FASEFASE-2019-EniserGS #fault #locality #named
DeepFault: Fault Localization for Deep Neural Networks (HFE, SG, AS0), pp. 171–191.
CAVCAV-2019-CeskaK #abstraction #analysis
Semi-quantitative Abstraction and Analysis of Chemical Reaction Networks (MC0, JK), pp. 475–496.
CAVCAV-2019-KatzHIJLLSTWZDK #analysis #framework #verification
The Marabou Framework for Verification and Analysis of Deep Neural Networks (GK, DAH, DI, KJ, CL, RL, PS, ST, HW0, AZ, DLD, MJK, CWB), pp. 443–452.
CAVCAV-2019-BackesBCDGHKKKK #analysis #reachability
Reachability Analysis for AWS-Based Networks (JB, SB, BC, CD, AG, AJH, TK, BK, EK, JK, SM, JR0, NR, JS, MAS, PS, PS, CV, BW), pp. 231–241.
CAVCAV-2019-GiannarakisBMW #fault tolerance #performance #refinement #verification
Efficient Verification of Network Fault Tolerance via Counterexample-Guided Refinement (NG, RB, RM, DW), pp. 305–323.
ICSTICST-2019-LeeHYKKY #integration #static analysis #using
Classifying False Positive Static Checker Alarms in Continuous Integration Using Convolutional Neural Networks (SL, SH, JY, TK, CJK, SY), pp. 391–401.
ICSAICSA-2018-TenbergenDOB #cyber-physical #modelling
View-Centric Context Modeling to Foster the Engineering of Cyber-Physical System Networks (BT, MD, PAO, JB), pp. 206–216.
JCDLJCDL-2018-DengTF #case study #interactive #library #social
Interaction on An Academic Social Networking Sites: A Study of ResearchGate Q&A on Library and Information Science (SD, JT, SF), pp. 25–28.
JCDLJCDL-2018-SiegelLPA
Extracting Scientific Figures with Distantly Supervised Neural Networks (NS, NL, RP, WA), pp. 223–232.
JCDLJCDL-2018-ZhangW #ranking
Ranking Scientific Papers and Venues in Heterogeneous Academic Networks by Mutual Reinforcement (FZ, SW), pp. 127–130.
EDMEDM-2018-PytlarzPPP #predict #student #transaction #what
What can we learn from college students' network transactions? Constructing useful features for student success prediction (IP, SP, MP, RP).
ICPCICPC-2018-OttAHBAFL #learning #programming language #using
Learning lexical features of programming languages from imagery using convolutional neural networks (JO, AA, PH, NB, HA, CF, EL), pp. 336–339.
ICSMEICSME-2018-DecanMC #dependence #evolution #on the
On the Evolution of Technical Lag in the npm Package Dependency Network (AD, TM, EC), pp. 404–414.
ICSMEICSME-2018-JansenHT #case study #detection #energy #evolution #predict #spreadsheet
Detecting and Predicting Evolution in Spreadsheets - A Case Study in an Energy Network Company (BJ, FH, ET), pp. 645–654.
MSRMSR-2018-DecanMC #dependence #on the #security
On the impact of security vulnerabilities in the npm package dependency network (AD, TM, EC), pp. 181–191.
SANERSANER-2018-KatzRS #decompiler #using
Using recurrent neural networks for decompilation (DSK, JR, ES), pp. 346–356.
SANERSANER-2018-LiSS #automation #requirements
Extracting features from requirements: Achieving accuracy and automation with neural networks (YL0, SS, GS), pp. 477–481.
SANERSANER-2018-NguyenNPN #source code
A deep neural network language model with contexts for source code (ATN0, TDN, HDP, TNN), pp. 323–334.
CIAACIAA-2018-Condon #algorithm #analysis #design #on the
On Design and Analysis of Chemical Reaction Network Algorithms (AC), pp. 1–3.
FMFM-2018-PardoSS #knowledge base #social
Timed Epistemic Knowledge Bases for Social Networks (RP, CS, GS), pp. 185–202.
IFM-2018-Galpin #formal method #modelling
Formal Modelling of Software Defined Networking (VG), pp. 172–193.
AIIDEAIIDE-2018-NeufeldMB #approach #behaviour #execution #hybrid
A Hybrid Approach to Planning and Execution in Dynamic Environments Through Hierarchical Task Networks and Behavior Trees (XN, SM, SB), pp. 201–207.
CHI-PLAYCHI-PLAY-2018-RoohiTKH #analysis
Neural Network Based Facial Expression Analysis of GameEvents: A Cautionary Tale (SR, JT, JMK, PH), pp. 429–437.
CoGCIG-2018-WanK #evaluation
Building Evaluation Functions for Chess and Shogi with Uniformity Regularization Networks (SW, TK), pp. 1–8.
CoGCIG-2018-WoofC #game studies #learning
Learning to Play General Video-Games via an Object Embedding Network (WW, KC), pp. 1–8.
FDGFDG-2018-PlumbKS #clustering #game studies #hybrid #multi #online #using
Hybrid network clusters using common gameplay for massively multiplayer online games (JNP, SKK, RS), p. 10.
CIKMCIKM-2018-0001CWCLJSZ #named #social
MEgo2Vec: Embedding Matched Ego Networks for User Alignment Across Social Networks (JZ0, BC, XW, HC0, CL, FJ, GS, YZ), pp. 327–336.
CIKMCIKM-2018-AdigaCKMRRS #modelling #probability #query #using
Inferring Probabilistic Contagion Models Over Networks Using Active Queries (AA, VCM, CJK, MVM, SSR, DJR, RES), pp. 377–386.
CIKMCIKM-2018-CaoCCTLL #assessment #behaviour #community #detection #enterprise
Behavior-based Community Detection: Application to Host Assessment In Enterprise Information Networks (CC, ZC, JC, LAT, CL, ZL), pp. 1977–1985.
CIKMCIKM-2018-ChaeKKL #collaboration #framework #generative #named
CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks (DKC, JSK, SWK, JTL), pp. 137–146.
CIKMCIKM-2018-ChenQLS #quote
“Bridge”: Enhanced Signed Directed Network Embedding (YC, TQ, HL, KS), pp. 773–782.
CIKMCIKM-2018-ChenSTPCS
Enhanced Network Embeddings via Exploiting Edge Labels (HC, XS, YT, BP, MC, SS), pp. 1579–1582.
CIKMCIKM-2018-ChenWH #graph #predict
Incorporating Corporation Relationship via Graph Convolutional Neural Networks for Stock Price Prediction (YC, ZW, XH), pp. 1655–1658.
CIKMCIKM-2018-CirsteaMMG0 #correlation #multi #using
Correlated Time Series Forecasting using Multi-Task Deep Neural Networks (RGC, DVM, GMM, CG, BY0), pp. 1527–1530.
CIKMCIKM-2018-DerrAT #modelling
Signed Network Modeling Based on Structural Balance Theory (TD, CCA, JT), pp. 557–566.
CIKMCIKM-2018-EsuliF0 #quantifier #sentiment
A Recurrent Neural Network for Sentiment Quantification (AE, AMF, FS0), pp. 1775–1778.
CIKMCIKM-2018-FanLFSL #identification #semantics
A Globalization-Semantic Matching Neural Network for Paraphrase Identification (MF, WL, YF, MS, PL0), pp. 2067–2075.
CIKMCIKM-2018-FanSW #detection
Abnormal Event Detection via Heterogeneous Information Network Embedding (SF, CS, XW0), pp. 1483–1486.
CIKMCIKM-2018-GalimbertiBBCG #mining
Mining (maximal) Span-cores from Temporal Networks (EG, AB, FB, CC, FG), pp. 107–116.
CIKMCIKM-2018-GaoJLD0 #identification #social
User Identification with Spatio-Temporal Awareness across Social Networks (XG, WJ, YL0, YD, WD0), pp. 1831–1834.
CIKMCIKM-2018-GuanJWC #graph
Shared Embedding Based Neural Networks for Knowledge Graph Completion (SG, XJ, YW, XC), pp. 247–256.
CIKMCIKM-2018-GuoCZGL #detection #social
Rumor Detection with Hierarchical Social Attention Network (HG, JC, YZ, JG, JL), pp. 943–951.
CIKMCIKM-2018-HidasiK #recommendation
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations (BH, AK), pp. 843–852.
CIKMCIKM-2018-HosseiniCWSS #named
HeteroMed: Heterogeneous Information Network for Medical Diagnosis (AH, TC0, WW, YS, MS), pp. 763–772.
CIKMCIKM-2018-HuangZZC #named #predict
DeepCrime: Attentive Hierarchical Recurrent Networks for Crime Prediction (CH0, JZ, YZ, NVC), pp. 1423–1432.
CIKMCIKM-2018-HuSZY #recommendation
Local and Global Information Fusion for Top-N Recommendation in Heterogeneous Information Network (BH, CS, WXZ, TY), pp. 1683–1686.
CIKMCIKM-2018-HuZY #collaboration #named #recommendation
CoNet: Collaborative Cross Networks for Cross-Domain Recommendation (GH, YZ, QY), pp. 667–676.
CIKMCIKM-2018-JiangW #named #query
RIN: Reformulation Inference Network for Context-Aware Query Suggestion (JYJ, WW0), pp. 197–206.
CIKMCIKM-2018-KangFYXCT #multi #named #ranking
X-Rank: Explainable Ranking in Complex Multi-Layered Networks (JK, SF, HY, YX, NC, HT), pp. 1959–1962.
CIKMCIKM-2018-KhattarKV018a #3d #recommendation #word
Weave&Rec: A Word Embedding based 3-D Convolutional Network for News Recommendation (DK, VK, VV, MG0), pp. 1855–1858.
CIKMCIKM-2018-LiGC #named
VTeller: Telling the Values Somewhere, Sometime in a Dynamic Network of Urban Systems (YL, TG, CXC), pp. 577–586.
CIKMCIKM-2018-Liu0SZ #predict
Hierarchical Complementary Attention Network for Predicting Stock Price Movements with News (QL, XC0, SS, SZ), pp. 1603–1606.
CIKMCIKM-2018-LiuCYZLS #detection #graph
Heterogeneous Graph Neural Networks for Malicious Account Detection (ZL, CC, XY, JZ, XL, LS), pp. 2077–2085.
CIKMCIKM-2018-LvZCXYL #predict
Homepage Augmentation by Predicting Links in Heterogenous Networks (JL, JZ, WC, QX, ZY, QL0), pp. 1611–1614.
CIKMCIKM-2018-MeiRCNMN #interactive #recommendation
An Attentive Interaction Network for Context-aware Recommendations (LM, PR, ZC, LN, JM0, JYN), pp. 157–166.
CIKMCIKM-2018-PalC
Label Propagation with Neural Networks (AP, DC), pp. 1671–1674.
CIKMCIKM-2018-SarkarB0 #on the
On Rich Clubs of Path-Based Centralities in Networks (SS, SB, AM0), pp. 567–576.
CIKMCIKM-2018-ShuaiLYLLY #behaviour #social
Newsfeed Filtering and Dissemination for Behavioral Therapy on Social Network Addictions (HHS, YCL, DNY, YFL, WCL, PSY), pp. 597–606.
CIKMCIKM-2018-SunJSPOW #multi #pointer #summary
Multi-Source Pointer Network for Product Title Summarization (FS, PJ, HS, CP, WO, XW), pp. 7–16.
CIKMCIKM-2018-Tang #probability #social
Stochastic Coupon Probing in Social Networks (ST), pp. 1023–1031.
CIKMCIKM-2018-Yu0LYL #adaptation #identification #recommendation #social
Adaptive Implicit Friends Identification over Heterogeneous Network for Social Recommendation (JY, MG0, JL, HY, HL0), pp. 357–366.
CIKMCIKM-2018-ZhangLNLX #graph #multi
Multiresolution Graph Attention Networks for Relevance Matching (TZ, BL, DN, KL, YX), pp. 933–942.
CIKMCIKM-2018-ZhangLS
Improve Network Embeddings with Regularization (YZ0, JL, OS), pp. 1643–1646.
CIKMCIKM-2018-ZhouYWBEYZW #named #personalisation #ranking
PRRE: Personalized Relation Ranking Embedding for Attributed Networks (SZ, HY, XW0, JB, ME, PY, JZ, CW0), pp. 823–832.
ECIRECIR-2018-DeyKGS #community #lifecycle #semantics #social #topic
Topic Lifecycle on Social Networks: Analyzing the Effects of Semantic Continuity and Social Communities (KD, SK, KG, RS), pp. 29–42.
ECIRECIR-2018-GalkoE #retrieval
Biomedical Question Answering via Weighted Neural Network Passage Retrieval (FG, CE), pp. 523–528.
ECIRECIR-2018-RepkeK #email
Bringing Back Structure to Free Text Email Conversations with Recurrent Neural Networks (TR, RK), pp. 114–126.
ICMLICML-2018-AroraCH #on the #optimisation
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization (SA, NC, EH), pp. 244–253.
ICMLICML-2018-Baity-JesiSGSAC
Comparing Dynamics: Deep Neural Networks versus Glassy Systems (MBJ, LS, MG, SS, GBA, CC, YL, MW, GB), pp. 324–333.
ICMLICML-2018-BalestrieroB
A Spline Theory of Deep Networks (RB, RGB), pp. 383–392.
ICMLICML-2018-BangS #generative #using
Improved Training of Generative Adversarial Networks using Representative Features (DB, HS), pp. 442–451.
ICMLICML-2018-BinkowskiMD
Autoregressive Convolutional Neural Networks for Asynchronous Time Series (MB, GM, PD), pp. 579–588.
ICMLICML-2018-BojanowskiJLS #generative #optimisation
Optimizing the Latent Space of Generative Networks (PB, AJ, DLP, AS), pp. 599–608.
ICMLICML-2018-CaiYZHY #architecture #performance
Path-Level Network Transformation for Efficient Architecture Search (HC, JY, WZ0, SH, YY0), pp. 677–686.
ICMLICML-2018-ChenBLR #adaptation #multi #named #normalisation
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks (ZC0, VB, CYL, AR), pp. 793–802.
ICMLICML-2018-ChenPS
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks (MC, JP, SSS), pp. 872–881.
ICMLICML-2018-ChenZS #graph #probability #reduction
Stochastic Training of Graph Convolutional Networks with Variance Reduction (JC, JZ0, LS), pp. 941–949.
ICMLICML-2018-DabneyOSM #learning
Implicit Quantile Networks for Distributional Reinforcement Learning (WD, GO, DS, RM), pp. 1104–1113.
ICMLICML-2018-DaiZGW #using
Compressing Neural Networks using the Variational Information Bottleneck (BD, CZ, BG, DPW), pp. 1143–1152.
ICMLICML-2018-DezfouliBN
Variational Network Inference: Strong and Stable with Concrete Support (AD, EVB, RN), pp. 1212–1221.
ICMLICML-2018-DiengRAB #named
Noisin: Unbiased Regularization for Recurrent Neural Networks (ABD, RR, JA, DMB), pp. 1251–1260.
ICMLICML-2018-DraxlerVSH #energy
Essentially No Barriers in Neural Network Energy Landscape (FD, KV, MS, FAH), pp. 1308–1317.
ICMLICML-2018-DuL #on the #polynomial #power of
On the Power of Over-parametrization in Neural Networks with Quadratic Activation (SSD, JDL), pp. 1328–1337.
ICMLICML-2018-FengWCS #learning #multi #parametricity #using
Nonparametric variable importance using an augmented neural network with multi-task learning (JF, BDW, MC, NS), pp. 1495–1504.
ICMLICML-2018-FurlanelloLTIA
Born-Again Neural Networks (TF, ZCL, MT, LI, AA), pp. 1602–1611.
ICMLICML-2018-GaoCL #named #optimisation
Spotlight: Optimizing Device Placement for Training Deep Neural Networks (YG, LC0, BL), pp. 1662–1670.
ICMLICML-2018-GaoW #learning #parallel
Parallel Bayesian Network Structure Learning (TG, DW), pp. 1671–1680.
ICMLICML-2018-GhoshYD #learning
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors (SG, JY, FDV), pp. 1739–1748.
ICMLICML-2018-GilraG #learning
Non-Linear Motor Control by Local Learning in Spiking Neural Networks (AG, WG), pp. 1768–1777.
ICMLICML-2018-HefnyM0SG #policy #predict
Recurrent Predictive State Policy Networks (AH, ZM, WS0, SSS, GJG), pp. 1954–1963.
ICMLICML-2018-HelfrichWY #orthogonal
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform (KH, DW, QY0), pp. 1974–1983.
ICMLICML-2018-HongRL #algorithm #distributed #higher-order #optimisation
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks (MH, MR, JDL), pp. 2014–2023.
ICMLICML-2018-JiaLQA
Exploring Hidden Dimensions in Parallelizing Convolutional Neural Networks (ZJ, SL, CRQ, AA), pp. 2279–2288.
ICMLICML-2018-JiangZLLF #data-driven #education #learning #named
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels (LJ0, ZZ, TL, LJL, LFF0), pp. 2309–2318.
ICMLICML-2018-JinKL #testing
Network Global Testing by Counting Graphlets (JJ, ZTK, SL), pp. 2338–2346.
ICMLICML-2018-JinYXYJFY #named #performance
WSNet: Compact and Efficient Networks Through Weight Sampling (XJ, YY, NX0, JY, NJ, JF, SY), pp. 2357–2366.
ICMLICML-2018-KhrulkovO #generative #geometry
Geometry Score: A Method For Comparing Generative Adversarial Networks (VK, IVO), pp. 2626–2634.
ICMLICML-2018-KondorT #on the
On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups (RK, ST), pp. 2752–2760.
ICMLICML-2018-KumarSJ #kernel #metric
Trainable Calibration Measures For Neural Networks From Kernel Mean Embeddings (AK, SS, UJ), pp. 2810–2819.
ICMLICML-2018-LakeB #composition
Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks (BML, MB), pp. 2879–2888.
ICMLICML-2018-LaurentB #linear
Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global (TL0, JvB), pp. 2908–2913.
ICMLICML-2018-LaurentB18a #multi
The Multilinear Structure of ReLU Networks (TL0, JvB), pp. 2914–2922.
ICMLICML-2018-LeePCXS
Gated Path Planning Networks (LL, EP, DSC, EPX, RS), pp. 2953–2961.
ICMLICML-2018-LiangSLS #classification #comprehension
Understanding the Loss Surface of Neural Networks for Binary Classification (SL, RS, YL, RS), pp. 2840–2849.
ICMLICML-2018-LiGD #bias #induction #learning
Explicit Inductive Bias for Transfer Learning with Convolutional Networks (XL0, YG, FD), pp. 2830–2839.
ICMLICML-2018-LiH #approach #learning
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks (QL, SH), pp. 2991–3000.
ICMLICML-2018-LuZLD #architecture #difference #equation #finite
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations (YL, AZ, QL, BD0), pp. 3282–3291.
ICMLICML-2018-MartinLV #approximate #clustering #performance
Fast Approximate Spectral Clustering for Dynamic Networks (LM, AL, PV), pp. 3420–3429.
ICMLICML-2018-MehrabiTY #approximate #bound #power of
Bounds on the Approximation Power of Feedforward Neural Networks (MM, AT, MIY), pp. 3450–3458.
ICMLICML-2018-MetzlerSVB #flexibility #named #retrieval #robust
prDeep: Robust Phase Retrieval with a Flexible Deep Network (CAM, PS, AV, RGB), pp. 3498–3507.
ICMLICML-2018-MiconiSC
Differentiable plasticity: training plastic neural networks with backpropagation (TM, KOS, JC), pp. 3556–3565.
ICMLICML-2018-MirmanGV #abstract interpretation #robust
Differentiable Abstract Interpretation for Provably Robust Neural Networks (MM, TG, MTV), pp. 3575–3583.
ICMLICML-2018-NguyenM0
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions (QN0, MCM, MH0), pp. 3737–3746.
ICMLICML-2018-NitandaS #functional
Functional Gradient Boosting based on Residual Network Perception (AN, TS), pp. 3816–3825.
ICMLICML-2018-OkunoHS #framework #learning #multi #probability
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks (AO, TH, HS), pp. 3885–3894.
ICMLICML-2018-OlivaDZPSXS
Transformation Autoregressive Networks (JBO, AD, MZ, BP, RS, EPX, JS), pp. 3895–3904.
ICMLICML-2018-OstrovskiDM #generative #modelling
Autoregressive Quantile Networks for Generative Modeling (GO, WD, RM), pp. 3933–3942.
ICMLICML-2018-Oymak #learning
Learning Compact Neural Networks with Regularization (SO), pp. 3963–3972.
ICMLICML-2018-PangDZ #analysis #linear
Max-Mahalanobis Linear Discriminant Analysis Networks (TP, CD, JZ0), pp. 4013–4022.
ICMLICML-2018-QiaoZ0WY #image #recognition #scalability
Gradually Updated Neural Networks for Large-Scale Image Recognition (SQ, ZZ, WS0, BW0, ALY), pp. 4185–4194.
ICMLICML-2018-QiuCCS #named
DCFNet: Deep Neural Network with Decomposed Convolutional Filters (QQ, XC, ARC, GS), pp. 4195–4204.
ICMLICML-2018-ReagenGAMRWB #encoding #named
Weightless: Lossy weight encoding for deep neural network compression (BR, UG, BA, MM, AMR, GYW, DB0), pp. 4321–4330.
ICMLICML-2018-SafranS
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks (IS, OS), pp. 4430–4438.
ICMLICML-2018-SajjadiPMS
Tempered Adversarial Networks (MSMS, GP, AM, BS), pp. 4448–4456.
ICMLICML-2018-Sanchez-Gonzalez #graph #physics
Graph Networks as Learnable Physics Engines for Inference and Control (ASG, NH, JTS, JM, MAR, RH, PWB), pp. 4467–4476.
ICMLICML-2018-SantoroHBML #reasoning
Measuring abstract reasoning in neural networks (AS, FH, DGTB, ASM, TPL), pp. 4477–4486.
ICMLICML-2018-SerraTR #bound #linear
Bounding and Counting Linear Regions of Deep Neural Networks (TS, CT, SR), pp. 4565–4573.
ICMLICML-2018-SewardUBJH #first-order #generative
First Order Generative Adversarial Networks (CS, TU, UB, NJ, SH), pp. 4574–4583.
ICMLICML-2018-SrinivasJALF #learning
Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control (AS, AJ, PA, SL, CF), pp. 4739–4748.
ICMLICML-2018-SunYDB #matrix
Convolutional Imputation of Matrix Networks (QS, MY, DLD, SPB), pp. 4825–4834.
ICMLICML-2018-TaoCHFC #generative
Chi-square Generative Adversarial Network (CT, LC, RH, JF, LC), pp. 4894–4903.
ICMLICML-2018-TeyeAS #estimation #nondeterminism #normalisation
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks (MT, HA, KS0), pp. 4914–4923.
ICMLICML-2018-WangVLGGZ
Adversarial Distillation of Bayesian Neural Network Posteriors (KCW, PV, JL, LG, RBG, RSZ), pp. 5177–5186.
ICMLICML-2018-WehrmannCB #classification #multi
Hierarchical Multi-Label Classification Networks (JW, RC, RCB), pp. 5225–5234.
ICMLICML-2018-WeinshallCA #education #learning
Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks (DW, GC, DA), pp. 5235–5243.
ICMLICML-2018-WeissGY #automaton #query #using
Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples (GW, YG, EY), pp. 5244–5253.
ICMLICML-2018-WengZCSHDBD #performance #robust #towards
Towards Fast Computation of Certified Robustness for ReLU Networks (TWW, HZ0, HC, ZS, CJH, LD, DSB, ISD), pp. 5273–5282.
ICMLICML-2018-WenHSZCL #predict #recognition
Deep Predictive Coding Network for Object Recognition (HW, KH, JS, YZ, EC, ZL), pp. 5263–5272.
ICMLICML-2018-XiaoBSSP #how
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks (LX, YB, JSD, SSS, JP), pp. 5389–5398.
ICMLICML-2018-XuLTSKJ #graph #learning #representation
Representation Learning on Graphs with Jumping Knowledge Networks (KX, CL, YT, TS, KiK, SJ), pp. 5449–5458.
ICMLICML-2018-YangK #modelling #process #relational
Dependent Relational Gamma Process Models for Longitudinal Networks (SY, HK), pp. 5547–5556.
ICMLICML-2018-YeS #approach
Variable Selection via Penalized Neural Network: a Drop-Out-One Loss Approach (MY, YS), pp. 5616–5625.
ICMLICML-2018-YoonJS18a #dataset #generative #modelling #multi #named #predict #using
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks (JY, JJ, MvdS), pp. 5685–5693.
ICMLICML-2018-ZhangLD #performance
Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization (JZ, QL, ISD), pp. 5801–5809.
ICMLICML-2018-ZhangNL #geometry
Tropical Geometry of Deep Neural Networks (LZ, GN, LHL), pp. 5819–5827.
ICPRICPR-2018-0001D #recognition
Deep Emotion Transfer Network for Cross-database Facial Expression Recognition (SL0, WD), pp. 3092–3099.
ICPRICPR-2018-0005GHL #feature model #image #segmentation
Feature Extraction and Grain Segmentation of Sandstone Images Based on Convolutional Neural Networks (FJ0, QG, HH, NL), pp. 2636–2641.
ICPRICPR-2018-Aldana-LopezCZG #approach #learning
Dynamic Learning Rate for Neural Networks: A Fixed-Time Stability Approach (RAL, LECM, JZ, DGG, AC), pp. 1378–1383.
ICPRICPR-2018-BallottaBVC #detection #image
Fully Convolutional Network for Head Detection with Depth Images (DB, GB, RV, RC), pp. 752–757.
ICPRICPR-2018-BandyopadhyayNM #axiom #social
A Generic Axiomatic Characterization for Measuring Influence in Social Networks (SB, RN, MNM), pp. 2606–2611.
ICPRICPR-2018-BhuniaBBKB0P #using
Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network (AKB, AB, AKB, AK, PB, PPR0, UP0), pp. 3639–3644.
ICPRICPR-2018-BhuniaBBKB0P18a #generative #image #using #word
Word Level Font-to-Font Image Translation using Convolutional Recurrent Generative Adversarial Networks (AKB, AKB, PB, AK, AB, PPR0, UP0), pp. 3645–3650.
ICPRICPR-2018-BiciciKA
Conditional Information Gain Networks (UCB, CK, LA), pp. 1390–1395.
ICPRICPR-2018-CaiLWY #estimation #multi
Joint Head Pose Estimation with Multi-task Cascaded Convolutional Networks for Face Alignment (ZC, QL, SW, BY), pp. 495–500.
ICPRICPR-2018-CaoFM #multi #recognition
An End-to-End Neural Network for Multi-line License Plate Recognition (YC, HF, HM), pp. 3698–3703.
ICPRICPR-2018-CaoLZSLS #detection #named #performance
ThinNet: An Efficient Convolutional Neural Network for Object Detection (SC, YL, CZ, QSS, PL, SS), pp. 836–841.
ICPRICPR-2018-ChangWZ #linear #performance #self
Piecewise Linear Units for Fast Self-Normalizing Neural Networks (YC, XW, SZ), pp. 429–434.
ICPRICPR-2018-ChengLYT #image #named #recursion
SESR: Single Image Super Resolution with Recursive Squeeze and Excitation Networks (XC, XL0, JY0, YT), pp. 147–152.
ICPRICPR-2018-ChenYY #classification #image
Semi-supervised convolutional neural networks with label propagation for image classification (LC, SY, MY0), pp. 1319–1324.
ICPRICPR-2018-CheQ #estimation #segmentation
Dynamic Projected Segmentation Networks For Hand Pose Estimation (YC, YQ), pp. 477–482.
ICPRICPR-2018-ChoiCT #authentication #mobile #probability #random
One-class Random Maxout Probabilistic Network for Mobile Touchstroke Authentication (SC, IC, ABJT), pp. 3359–3364.
ICPRICPR-2018-ChooSJC #classification #multi
Multi-scale Recurrent Encoder-Decoder Network for Dense Temporal Classification (SKC, WS, DjJ, NIC), pp. 103–108.
ICPRICPR-2018-CombinidoMA #approach #image #using
A Convolutional Neural Network Approach for Estimating Tropical Cyclone Intensity Using Satellite-based Infrared Images (JSC, JRM, JA), pp. 1474–1480.
ICPRICPR-2018-CuiB00JH #graph #hybrid #kernel #learning
A Deep Hybrid Graph Kernel Through Deep Learning Networks (LC, LB0, LR0, YW0, YJ0, ERH), pp. 1030–1035.
ICPRICPR-2018-CuiLZLXGJZ #classification
Classification Guided Deep Convolutional Network for Compressed Sensing (WC, SL, SZ, YL0, HX, XG, FJ0, DZ), pp. 2905–2910.
ICPRICPR-2018-CuiZZH #learning #multi #recognition #using
Multi-source Learning for Skeleton -based Action Recognition Using Deep LSTM Networks (RC, AZ, SZ, GH0), pp. 547–552.
ICPRICPR-2018-DaiHGX0GQ #detection #multi #segmentation
Fused Text Segmentation Networks for Multi-oriented Scene Text Detection (YD, ZH, YG, YX, KC0, JG, WQ), pp. 3604–3609.
ICPRICPR-2018-DasRBP #classification #documentation #image #learning
Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks (AD, SR, UB, SKP), pp. 3180–3185.
ICPRICPR-2018-DehzangiT #detection #representation #using
Driver Distraction Detection using MEL Cepstrum Representation of Galvanic Skin Responses and Convolutional Neural Networks (OD, MT), pp. 1481–1486.
ICPRICPR-2018-DingLXKS #generative #recognition #robust #speech #towards
Mutual-optimization Towards Generative Adversarial Networks For Robust Speech Recognition (KD, NL, YX, DK, KS), pp. 2699–2704.
ICPRICPR-2018-DongZL #recognition
Dynamic Facial Expression Recognition Based on Convolutional Neural Networks with Dense Connections (JD, HZ, LL), pp. 3433–3438.
ICPRICPR-2018-Dou0 #2d #3d #predict
2D and 3D Convolutional Neural Network Fusion for Predicting the Histological Grade of Hepatocellular Carcinoma (TD, WZ0), pp. 3832–3837.
ICPRICPR-2018-Du0CHW #adaptation #visual notation
Object-Adaptive LSTM Network for Visual Tracking (YD, YY0, SC, YH, HW), pp. 1719–1724.
ICPRICPR-2018-DuSZ #contest #identification #multi
Which Part is Better: Multi-Part Competition Network for person Re-Identification (PD, YS, YZ0), pp. 1634–1639.
ICPRICPR-2018-EleziTVP #learning
Transductive Label Augmentation for Improved Deep Network Learning (IE, AT, SV, MP), pp. 1432–1437.
ICPRICPR-2018-ElmogyZEE #3d #automation #classification #framework
An Automated Classification Framework for Pressure Ulcer Tissues Based on 3D Convolutional Neural Network (ME, BGZ, ASE, AEB), pp. 2356–2361.
ICPRICPR-2018-Fan
Deep Epitome for Unravelling Generalized Hamming Network (LF), pp. 409–416.
ICPRICPR-2018-FangYZZ
Diversified Dual Domain-Adversarial Neural Networks (YF, QY, WZ, ZZ), pp. 615–620.
ICPRICPR-2018-GaoCZLL #3d
Background Subtraction via 3D Convolutional Neural Networks (YG, HC, XZ0, LL, ZL), pp. 1271–1276.
ICPRICPR-2018-GarrettR #3d #performance #using
Fast Descriptor Extraction for Contextless 3D Registration Using a Fully Convolutional Network (TG, RR), pp. 1211–1216.
ICPRICPR-2018-GasparettoRBPCB
Cross-Dataset Data Augmentation for Convolutional Neural Networks Training (AG, DR, FB, MP, LC, MB, EU, AA), pp. 910–915.
ICPRICPR-2018-GrelssonF #exponential #learning #linear
Improved Learning in Convolutional Neural Networks with Shifted Exponential Linear Units (ShELUs) (BG, MF), pp. 517–522.
ICPRICPR-2018-HanXW #generative #learning #multi #representation
Learning Multi-view Generator Network for Shared Representation (TH0, XX, YNW), pp. 2062–2068.
ICPRICPR-2018-HanXZL #composition #image #learning
Learning Intrinsic Image Decomposition by Deep Neural Network with Perceptual Loss (GH, XX, WSZ, JL), pp. 91–96.
ICPRICPR-2018-HanZG #classification #composition #multi
Multi-Frequency Decomposition with Fully Convolutional Neural Network for Time Series Classification (YH, SZ, ZG), pp. 284–289.
ICPRICPR-2018-HeGG #learning
Structure Learning of Bayesian Networks by Finding the Optimal Ordering (CCH, XGG, ZgG), pp. 177–182.
ICPRICPR-2018-HouCLWX #predict
Predicting Traffic Flow via Ensemble Deep Convolutional Neural Networks with Spatio-temporal Joint Relations (JH, JC, SL, JW, QX), pp. 1487–1492.
ICPRICPR-2018-HuiWG #image
Two-Stage Convolutional Network for Image Super-Resolution (ZH, XW, XG), pp. 2670–2675.
ICPRICPR-2018-JeongJLCY0 #estimation
Selective Ensemble Network for Accurate Crowd Density Estimation (JJ, HJ, JL, JC, SY, JYC0), pp. 320–325.
ICPRICPR-2018-JiangWYSLGFZ #recursion
Recursive Inception Network for Super-Resolution (TJ, XW, ZY, WS, GL, SG, HF, QZ), pp. 2759–2764.
ICPRICPR-2018-JiQYKZ #automation #image #segmentation #using
Automatic Prostate Segmentation on MR Images Using Enhanced Holistically-Nested Networks (DJ, JQ, JY, TK, SZ), pp. 3820–3825.
ICPRICPR-2018-JiuSQ #image
Deep Context Networks for Image Annotation (MJ, HS, LQ), pp. 2422–2427.
ICPRICPR-2018-JollyIKU #design #how #question
How do Convolutional Neural Networks Learn Design? (SJ, BKI, RK, SU), pp. 1085–1090.
ICPRICPR-2018-JyotiD #automation #estimation #geometry #using
Automatic Eye Gaze Estimation using Geometric & Texture-based Networks (SJ, AD), pp. 2474–2479.
ICPRICPR-2018-KasemHJ #image #towards
Revised Spatial Transformer Network towards Improved Image Super-resolutions (HMK, KWH, JJ), pp. 2688–2692.
ICPRICPR-2018-KhalidY #multi #recognition
Multi-Modal Three-Stream Network for Action Recognition (MUK, JY), pp. 3210–3215.
ICPRICPR-2018-KonwerBBBB0P #generative #using
Staff line Removal using Generative Adversarial Networks (AK, AKB, AB, AKB, PB, PPR0, UP0), pp. 1103–1108.
ICPRICPR-2018-KungMZ #adaptation #array
Adaptive Tiling: Applying Fixed-size Systolic Arrays To Sparse Convolutional Neural Networks (HTK, BM, SQZ), pp. 1006–1011.
ICPRICPR-2018-LengK
Confidence-Driven Network for Point-to-Set Matching (ML, IAK), pp. 3414–3420.
ICPRICPR-2018-LiaoZWN #detection #named #recognition
Uniface: A Unified Network for Face Detection and Recognition (ZL, PZ, QW, BN), pp. 3531–3536.
ICPRICPR-2018-LiciottiPPFZ #segmentation #semantics #using
Convolutional Networks for Semantic Heads Segmentation using Top-View Depth Data in Crowded Environment (DL, MP, RP, EF, PZ), pp. 1384–1389.
ICPRICPR-2018-LiCQWW #adaptation #learning #semantics
Cross-domain Semantic Feature Learning via Adversarial Adaptation Networks (RL, WmC0, SQ, HSW, SW), pp. 37–42.
ICPRICPR-2018-LiCZC #detection
Mammographic mass detection based on convolution neural network (YL, HC, LZ, LC), pp. 3850–3855.
ICPRICPR-2018-LiHLHS #consistency #generative
Global and Local Consistent Age Generative Adversarial Networks (PL, YH, QL0, RH, ZS), pp. 1073–1078.
ICPRICPR-2018-LiHY0CHWL #segmentation
Skin Lesion Segmentation via Dense Connected Deconvolutional Network (HL, XH, ZY, FZ0, JZC, LH, TW, BL), pp. 671–675.
ICPRICPR-2018-LingLZG #classification #image #learning
Semi-Supervised Learning via Convolutional Neural Network for Hyperspectral Image Classification (ZL, XL, WZ, SG), pp. 1–6.
ICPRICPR-2018-LingLZL #estimation #image #performance
Joint Haze-relevant Features Selection and Transmission Estimation via Deep Belief Network for Efficient Single Image Dehazing (ZL, XL, WZ, ML), pp. 133–139.
ICPRICPR-2018-LiuDJQ #classification #image #visual notation
Visual Tree Convolutional Neural Network in Image Classification (YL, YD, RJ, PQ), pp. 758–763.
ICPRICPR-2018-LiuGCL #generative
An Extensive Study of Cycle-Consistent Generative Networks for Image-to-Image Translation (YL0, YG, WC, MSL), pp. 219–224.
ICPRICPR-2018-LiuHH #classification
Lifting Scheme Based Deep Network Model for Remote Sensing Imagery Classification (XL, BH, CH), pp. 688–693.
ICPRICPR-2018-LiuMHWLB
Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting (XL, MM, LH, JvdW, AML0, ADB), pp. 2262–2268.
ICPRICPR-2018-LiuMXP #image #semantics #synthesis
Semantic Image Synthesis via Conditional Cycle-Generative Adversarial Networks (XL, GM, SX, CP), pp. 988–993.
ICPRICPR-2018-LiuRYXCY #graph #image
Fully convolutional network and graph-based method for co-segmentation of retinal layer on macular OCT images (YL, GR, GY, XX, XC, YY), pp. 3081–3085.
ICPRICPR-2018-LiW #estimation #image
Local Regression Based Hourglass Network for Hand Pose Estimation from a Single Depth Image (JL, ZW), pp. 1767–1772.
ICPRICPR-2018-LiZXGX18a #recognition #speech
Recurrent Neural Network Based Small-footprint Wake-up-word Speech Recognition System with a Score Calibration Method (CL, LZ, SX, PG, BX0), pp. 3222–3227.
ICPRICPR-2018-LocBO #documentation #image #security #using
Document Images Watermarking for Security Issue using Fully Convolutional Networks (CVL, JCB, JMO), pp. 1091–1096.
ICPRICPR-2018-LuoKW #generative #image #synthesis
Traffic Sign Image Synthesis with Generative Adversarial Networks (HL, QK, FW), pp. 2540–2545.
ICPRICPR-2018-LuoY #architecture #performance #segmentation
Fast Skin Lesion Segmentation via Fully Convolutional Network with Residual Architecture and CRF (WL, MY), pp. 1438–1443.
ICPRICPR-2018-MaGLLZ #3d #multi #named #segmentation #semantics
3DMAX-Net: A Multi-Scale Spatial Contextual Network for 3D Point Cloud Semantic Segmentation (YM, YG, YL, ML, JZ), pp. 1560–1566.
ICPRICPR-2018-MaH #generative #using
Perceptual Face Completion using a Local-Global Generative Adversarial Network (RM, HH0), pp. 1670–1675.
ICPRICPR-2018-MaierSSWSCF #learning #precise #towards #using
Precision Learning: Towards Use of Known Operators in Neural Networks (AKM, FS, CS, TW, SS, JHC, RF), pp. 183–188.
ICPRICPR-2018-MaLHY #estimation #hybrid #using
Image-based Air Pollution Estimation Using Hybrid Convolutional Neural Network (JM, KL, YH, JY), pp. 471–476.
ICPRICPR-2018-ManessiRBNS #automation
Automated Pruning for Deep Neural Network Compression (FM, AR, SB, PN, RS), pp. 657–664.
ICPRICPR-2018-MaWWW #detection #multi #named #performance
MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object Detection (WM, YW, ZW, GW), pp. 2510–2515.
ICPRICPR-2018-MingCLVB #detection #interactive #liveness #named #verification
FaceLiveNet: End-to-End Networks Combining Face Verification with Interactive Facial Expression-Based Liveness Detection (ZM, JC, MML, MV, JCB), pp. 3507–3512.
ICPRICPR-2018-MohantyDG18a #detection #robust
Robust Scene Text Detection with Deep Feature Pyramid Network and CNN based NMS Model (SM, TD, HPG), pp. 3741–3746.
ICPRICPR-2018-NabilIMSQS #detection #random
Deep Recurrent Electricity Theft Detection in AMI Networks with Random Tuning of Hyper-parameters (MN, MI0, MMEAM, MS, KAQ, ES), pp. 740–745.
ICPRICPR-2018-OGormanW #video
Balancing Video Analytics Processing and Bandwidth for Edge-Cloud Networks (LO, XW), pp. 2618–2623.
ICPRICPR-2018-ParkAMLCP0PK #generative #named
MMGAN: Manifold-Matching Generative Adversarial Networks (NP, AA, JRAM, KL, JC, DKP, TC0, HP, YK), pp. 1343–1348.
ICPRICPR-2018-PassalisT #information management #similarity #using
Neural Network Knowledge Transfer using Unsupervised Similarity Matching (NP, AT), pp. 716–721.
ICPRICPR-2018-PereraAP #generative #multi #named #using
In2I: Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks (PP, MA, VMP), pp. 140–146.
ICPRICPR-2018-Pham0V #graph #memory management #predict #process
Graph Memory Networks for Molecular Activity Prediction (TP, TT0, SV), pp. 639–644.
ICPRICPR-2018-PiantadosiSS #segmentation
Breast Segmentation in MRI via U-Net Deep Convolutional Neural Networks (GP, MS, CS), pp. 3917–3922.
ICPRICPR-2018-PramerdorferKL #3d #bound #classification #multi
Multi-View Classification and 3D Bounding Box Regression Networks (CP, MK, MVL), pp. 734–739.
ICPRICPR-2018-QiuXC #flexibility #linear #named
FReLU: Flexible Rectified Linear Units for Improving Convolutional Neural Networks (SQ, XX, BC), pp. 1223–1228.
ICPRICPR-2018-RhifWF #3d #recognition #sequence #using
Action Recognition from 3D Skeleton Sequences using Deep Networks on Lie Group Features (MR, HW, IRF), pp. 3427–3432.
ICPRICPR-2018-RibaFLF #graph #learning #message passing
Learning Graph Distances with Message Passing Neural Networks (PR, AF0, JL0, AF), pp. 2239–2244.
ICPRICPR-2018-Robles-KellyW #image
A Convolutional Neural Network for Pixelwise Illuminant Recovery in Colour and Spectral Images (ARK, RW), pp. 109–114.
ICPRICPR-2018-ShiLLLM #recognition
Weather Recognition Based on Edge Deterioration and Convolutional Neural Networks (YS, YL, JL, XL, YLM), pp. 2438–2443.
ICPRICPR-2018-ShiWDYL #generative
Data Augmentation with Improved Generative Adversarial Networks (HS, LW, GD, FY, XL), pp. 73–78.
ICPRICPR-2018-ShiZWTYZ #lens #modelling
Radial Lens Distortion Correction by Adding a Weight Layer with Inverted Foveal Models to Convolutional Neural Networks (YS, DZ, JW, XT, XY, HZ), pp. 1–6.
ICPRICPR-2018-ShuiWYHGLZ #3d #multi #segmentation
3D Shape Segmentation Based on Viewpoint Entropy and Projective Fully Convolutional Networks Fusing Multi-view Features (PS, PW, FY, BH, YG, KL, YZ0), pp. 1056–1061.
ICPRICPR-2018-SinhaNGG #analysis #hybrid #navigation #performance
Hybrid Path Planner for Efficient Navigation in Urban Road Networks through Analysis of Trajectory Traces (SS, MKN, SG, SKG), pp. 3250–3255.
ICPRICPR-2018-SoleymaniDKDN #abstraction #identification #multimodal
Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification (SS, AD, HK, JMD, NMN), pp. 3469–3476.
ICPRICPR-2018-SongZR #bound
UAV Target Tracking with A Boundary-Decision Network (KS, WZ0, XR), pp. 2576–2581.
ICPRICPR-2018-SunZY #automation #embedded #generative
Pyramid Embedded Generative Adversarial Network for Automated Font Generation (DS, QZ, JY), pp. 976–981.
ICPRICPR-2018-SwamiD #image #named
CANDY: Conditional Adversarial Networks based End-to-End System for Single Image Haze Removal (KS, SKD), pp. 3061–3067.
ICPRICPR-2018-TongT #image #recognition
Reservoir Computing with Untrained Convolutional Neural Networks for Image Recognition (ZT, GT), pp. 1289–1294.
ICPRICPR-2018-TsangDX #modelling
Recurrent Neural Networks for Financial Time-Series Modelling (GT, JD, XX), pp. 892–897.
ICPRICPR-2018-Wang #named
ReNN: Rule-embedded Neural Networks (HW), pp. 824–829.
ICPRICPR-2018-WangBZZ #generative
Generating Facial Line-drawing with Convolutional Neural Networks (YW, XB, LZ, SZ), pp. 513–516.
ICPRICPR-2018-WangCYX #self
Self-Attention Based Network for Punctuation Restoration (FW0, WC0, ZY0, BX0), pp. 2803–2808.
ICPRICPR-2018-WangGZXH #correlation #named #visual notation
SPCNet: Scale Position Correlation Network for End-to-End Visual Tracking (QW, JG, MZ, JX, WH), pp. 1803–1808.
ICPRICPR-2018-WangLLL #detection #multi
Anchor Free Network for Multi-Scale Face Detection (CW, ZL, SL, SL), pp. 1554–1559.
ICPRICPR-2018-WangXLTXX #detection #multi
Multi-scale Fusion with Context-aware Network for Object Detection (HW, JX, LL, YT, DX, SX), pp. 2486–2491.
ICPRICPR-2018-WangZGW #detection #image #multi #parametricity #using
Fully convolutional neural networks for prostate cancer detection using multi-parametric magnetic resonance images: an initial investigation (YW, BZ, DG, JW), pp. 3814–3819.
ICPRICPR-2018-WangZL #detection #generative
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks (CW, YMZ, CLL), pp. 1121–1126.
ICPRICPR-2018-WuLXFPL #predict
Context-Aware Attention LSTM Network for Flood Prediction (YW, ZL, WX, JF, SP, TL), pp. 1301–1306.
ICPRICPR-2018-WuXFPL #predict
Local and Global Bayesian Network based Model for Flood Prediction (YW, WX, JF, SP, TL), pp. 225–230.
ICPRICPR-2018-WuZ #generative
Deep Generative Adversarial Networks for the Sparse Signal Denoising (KW, CZ), pp. 1127–1132.
ICPRICPR-2018-XiaoLLLK #algorithm
Single-image Dehazing Algorithm Based on Convolutional Neural Networks (JX, LL, EL, JL, RK), pp. 1259–1264.
ICPRICPR-2018-XiaoW #animation #speech
Dense Convolutional Recurrent Neural Network for Generalized Speech Animation (LX, ZW), pp. 633–638.
ICPRICPR-2018-XieHLHYL #classification
Deeply Supervised Residual Network for HEp-2 Cell Classification (HX, YH, HL, TH, ZY, BL), pp. 699–703.
ICPRICPR-2018-XuPYL #process #recognition
Human Activity Recognition Based On Convolutional Neural Network (WX, YP, YY, YL), pp. 165–170.
ICPRICPR-2018-XuTY #recognition
Beyond Two-stream: Skeleton-based Three-stream Networks for Action Recognition in Videos (JX, KT, HY), pp. 1567–1573.
ICPRICPR-2018-XuW
Target Group Distribution Pattern Discovery via Convolutional Neural Network (XX, WW), pp. 266–271.
ICPRICPR-2018-XuWJW #visual notation
Visual Tracking by Combining the Structure-Aware Network and Spatial-Temporal Regression (DX, LW, MJ, QW), pp. 1912–1917.
ICPRICPR-2018-XuWZWZR0CH
Depth-based Subgraph Convolutional Neural Networks (CX, DW, ZZ, BW, DZ, GR, LB0, LC, ERH), pp. 1024–1029.
ICPRICPR-2018-XuZZ #image #recognition #using
Screen-rendered text images recognition using a deep residual network based segmentation-free method (XX0, JZ, HZ), pp. 2741–2746.
ICPRICPR-2018-YamadaIYK #segmentation #using
Texture Segmentation using Siamese Network and Hierarchical Region Merging (RY, HI, NY, TK), pp. 2735–2740.
ICPRICPR-2018-Yang0K #generative #multi
Multi-scale Generative Adversarial Networks for Crowd Counting (JY, YZ0, SYK), pp. 3244–3249.
ICPRICPR-2018-YangLPKWSLDCTZ #3d #automation #image #segmentation
Automatic Segmentation of Kidney and Renal Tumor in CT Images Based on 3D Fully Convolutional Neural Network with Pyramid Pooling Module (GY, GL, TP, YK, JW, HS, LL, JLD, JLC, LT, XZ), pp. 3790–3795.
ICPRICPR-2018-YangY
Enhanced Network Embedding with Text Information (SY, BY), pp. 326–331.
ICPRICPR-2018-YanJY #3d #detection #generative
3D Convolutional Generative Adversarial Networks for Detecting Temporal Irregularities in Videos (MY, XJ, JY), pp. 2522–2527.
ICPRICPR-2018-YanWSLZ #image #learning #using
Image Captioning using Adversarial Networks and Reinforcement Learning (SY, FW, JSS, WL, BZ), pp. 248–253.
ICPRICPR-2018-YuLH #comparison
Curvature-based Comparison of Two Neural Networks (TY, HL, JEH), pp. 441–447.
ICPRICPR-2018-ZhangCSZWHJL #assessment #named #performance #quality
SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks (TZ, JC, DFS, KZ, AW, PH, AJ, BCL), pp. 2314–2319.
ICPRICPR-2018-ZhangCZ #architecture #recognition
Temporal Inception Architecture for Action Recognition with Convolutional Neural Networks (WZ, JC, HZ), pp. 3216–3221.
ICPRICPR-2018-ZhangDKQL #detection #multi
Global Contrast Enhancement Detection via Deep Multi-Path Network (CZ, DD, LK, HQ, SL), pp. 2815–2820.
ICPRICPR-2018-ZhangFQS #generative #image
Wasserstein Generative Recurrent Adversarial Networks for Image Generating (CZ, YF, BQ, JS), pp. 242–247.
ICPRICPR-2018-ZhangHLZ #detection
Attention-based Neural Network for Traffic Sign Detection (JZ, LH, JL, YZ), pp. 1839–1844.
ICPRICPR-2018-ZhangJCXP #approach #graph #kernel #learning
Kernel-Weighted Graph Convolutional Network: A Deep Learning Approach for Traffic Forecasting (QZ, QJ, JC, SX, CP), pp. 1018–1023.
ICPRICPR-2018-ZhangLXQ #named
LHONE: Label Homophily Oriented Network Embedding (LZ, XL, JX, YQ), pp. 665–670.
ICPRICPR-2018-ZhangLZXSL #classification
Spatial Pyramid Dilated Network for Pulmonary Nodule Malignancy Classification (GZ, YL, DZ, YX, YS, JL), pp. 3911–3916.
ICPRICPR-2018-ZhangSOS #identification #using
Person Re-identification Using Two-Stage Convolutional Neural Network (YZ, JS, DO, HTS), pp. 3341–3346.
ICPRICPR-2018-ZhangSW #linear #problem
Cascade Deep Networks for Sparse Linear Inverse Problems (HZ, HS, WW), pp. 812–817.
ICPRICPR-2018-ZhangWGWXL #detection #effectiveness #learning
An Effective Deep Learning Based Scheme for Network Intrusion Detection (HZ, CQW, SG, ZW, YX, YL), pp. 682–687.
ICPRICPR-2018-ZhangYLQZC #detection
Single Shot Feature Aggregation Network for Underwater Object Detection (LZ, XY0, ZL, LQ, HZ, CC), pp. 1906–1911.
ICPRICPR-2018-ZhangZDD #analysis #online #recognition
Trajectory-based Radical Analysis Network for Online Handwritten Chinese Character Recognition (JZ, YZ, JD, LD), pp. 3681–3686.
ICPRICPR-2018-ZhangZDW #classification #image
Image Exploration Procedure Classification with Spike-timing Neural Network for the Blind (TZ, TZ, BSD, JPW), pp. 3256–3261.
ICPRICPR-2018-ZhangZW #classification #lightweight #named
LD-CNN: A Lightweight Dilated Convolutional Neural Network for Environmental Sound Classification (XZ, YZ, WW), pp. 373–378.
ICPRICPR-2018-ZhanLL #recognition #string #using
Handwritten Digit String Recognition using Convolutional Neural Network (HZ, SL, YL), pp. 3729–3734.
ICPRICPR-2018-ZhaoMS #classification #image #named
Retraining: A Simple Way to Improve the Ensemble Accuracy of Deep Neural Networks for Image Classification (KZ, TM, ES), pp. 860–867.
ICPRICPR-2018-ZhaoYT #order #predict #using
Pen Tip Motion Prediction for Handwriting Drawing Order Recovery using Deep Neural Network (BZ, MY, JT), pp. 704–709.
ICPRICPR-2018-ZhengZ #approach #classification
Accelerating the Classification of Very Deep Convolutional Network by A Cascading Approach (WZ, ZZ), pp. 355–360.
ICPRICPR-2018-ZhongJ0JX #fine-grained #image #multi #recognition
A Multi-part Convolutional Attention Network for Fine-Grained Image Recognition (WZ, LJ, TZ0, JJ, HX), pp. 1857–1862.
ICPRICPR-2018-ZhongYZ
Merging Neurons for Structure Compression of Deep Networks (GZ0, HY, HZ0), pp. 1462–1467.
ICPRICPR-2018-ZhuZ #detection
A Comprehensive Study on Upper-Body Detection with Deep Neural Networks (YZ, LZ), pp. 171–176.
ICPRICPR-2018-ZhuZLLZC #identification
A Shortly and Densely Connected Convolutional Neural Network for Vehicle Re-identification (JZ, HZ, ZL, SL, LZ, CC), pp. 3285–3290.
KDDKDD-2018-0022PYT #algorithm #effectiveness #optimisation
Network Connectivity Optimization: Fundamental Limits and Effective Algorithms (CC0, RP, LY, HT), pp. 1167–1176.
KDDKDD-2018-ChenGCSSJ #3d #image
Voxel Deconvolutional Networks for 3D Brain Image Labeling (YC, HG, LC, MS, DS, SJ), pp. 1226–1234.
KDDKDD-2018-ChenLB #social
Quantifying and Minimizing Risk of Conflict in Social Networks (XC, JL, TDB), pp. 1197–1205.
KDDKDD-2018-ChenYWWNL #metric #named #predict
PME: Projected Metric Embedding on Heterogeneous Networks for Link Prediction (HC, HY, WW0, HW0, QVHN, XL), pp. 1177–1186.
KDDKDD-2018-ChuHHWP #consistency #linear
Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution (LC, XH, JH, LW, JP), pp. 1244–1253.
KDDKDD-2018-ConteFGMSU #similarity
Node Similarity with q -Grams for Real-World Labeled Networks (AC, GF, RG, AM, KS, TU), pp. 1282–1291.
KDDKDD-2018-ConteMSGMV #community #detection #named #scalability
D2K: Scalable Community Detection in Massive Networks via Small-Diameter k-Plexes (AC, TDM, DDS, RG, AM, LV), pp. 1272–1281.
KDDKDD-2018-DizajiWH #generative
Semi-Supervised Generative Adversarial Network for Gene Expression Inference (KGD, XW, HH), pp. 1435–1444.
KDDKDD-2018-GaoH #self
Self-Paced Network Embedding (HG, HH), pp. 1406–1415.
KDDKDD-2018-GaoWJ #graph #scalability
Large-Scale Learnable Graph Convolutional Networks (HG, ZW, SJ), pp. 1416–1424.
KDDKDD-2018-GongW #analysis #behaviour #modelling #sentiment #social
When Sentiment Analysis Meets Social Network: A Holistic User Behavior Modeling in Opinionated Data (LG, HW), pp. 1455–1464.
KDDKDD-2018-HarelR #prototype #using
Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks (SH, KR), pp. 331–339.
KDDKDD-2018-LeeGZ #generative #query
Rare Query Expansion Through Generative Adversarial Networks in Search Advertising (MCL, BG, RZ), pp. 500–508.
KDDKDD-2018-Lian0ZGCT0 #higher-order #proximity
High-order Proximity Preserving Information Network Hashing (DL, KZ0, VWZ, YG, LC, IWT, XX0), pp. 1744–1753.
KDDKDD-2018-LiuHLH #induction #on the #taxonomy
On Interpretation of Network Embedding via Taxonomy Induction (NL, XH, JL, XH), pp. 1812–1820.
KDDKDD-2018-LiuHWH #self
Content to Node: Self-Translation Network Embedding (JL0, ZH, LW, YH), pp. 1794–1802.
KDDKDD-2018-LiuKY #social
Active Opinion Maximization in Social Networks (XL, XK, PSY), pp. 1840–1849.
KDDKDD-2018-LiY #classification #learning #policy
Learning Adversarial Networks for Semi-Supervised Text Classification via Policy Gradient (YL, JY), pp. 1715–1723.
KDDKDD-2018-LuoCTSLCY #information management #invariant #learning #named
TINET: Learning Invariant Networks via Knowledge Transfer (CL, ZC, LAT, AS, ZL, HC, JY), pp. 1890–1899.
KDDKDD-2018-MaCW0 #taxonomy
Hierarchical Taxonomy Aware Network Embedding (JM, PC0, XW0, WZ0), pp. 1920–1929.
KDDKDD-2018-MolinoZW #named #ranking
COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks (PM, HZ, YCW), pp. 586–595.
KDDKDD-2018-RaoTL #comprehension #framework #learning #multi #platform #query
Multi-Task Learning with Neural Networks for Voice Query Understanding on an Entertainment Platform (JR, FT, JL), pp. 636–645.
KDDKDD-2018-Sanei-MehriST
Butterfly Counting in Bipartite Networks (SVSM, AES, ST), pp. 2150–2159.
KDDKDD-2018-ShashikumarSCN #bidirectional #detection #using
Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks (SPS, AJS, GDC, SN), pp. 715–723.
KDDKDD-2018-ShenLOLZC #framework #named #novel #predict
StepDeep: A Novel Spatial-temporal Mobility Event Prediction Framework based on Deep Neural Network (BS, XL, YO, ML, WZ, KMC), pp. 724–733.
KDDKDD-2018-ShiZGZ0 #learning
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks (YS, QZ, FG, CZ0, JH0), pp. 2190–2199.
KDDKDD-2018-SunBZWZ #modelling #multi
Subspace Network: Deep Multi-Task Censored Regression for Modeling Neurodegenerative Diseases (MS, IMB, LZ, ZW, JZ), pp. 2259–2268.
KDDKDD-2018-TayLH #multi #recommendation
Multi-Pointer Co-Attention Networks for Recommendation (YT, ATL, SCH), pp. 2309–2318.
KDDKDD-2018-TayTH #multi
Multi-Cast Attention Networks (YT, LAT, SCH), pp. 2299–2308.
KDDKDD-2018-TomasiTSV
Latent Variable Time-varying Network Inference (FT, VT, SS, AV), pp. 2338–2346.
KDDKDD-2018-TuCWY0 #equivalence #recursion
Deep Recursive Network Embedding with Regular Equivalence (KT, PC0, XW0, PSY, WZ0), pp. 2357–2366.
KDDKDD-2018-WangMJYXJSG #detection #multi #named
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection (YW, FM, ZJ, YY0, GX, KJ, LS, JG0), pp. 849–857.
KDDKDD-2018-WangWLW #analysis #composition #multi
Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis (JW, ZW, JL, JW), pp. 2437–2446.
KDDKDD-2018-WangWW
Inferring Metapopulation Propagation Network for Intra-city Epidemic Control and Prevention (JW, XW, JW), pp. 830–838.
KDDKDD-2018-WangYHLWH #memory management #recommendation #streaming
Neural Memory Streaming Recommender Networks with Adversarial Training (QW, HY, ZH, DL, HW, ZH), pp. 2467–2475.
KDDKDD-2018-WangZHZ #learning #recommendation
Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation (LW, WZ0, XH, HZ), pp. 2447–2456.
KDDKDD-2018-WongPKFJ #biology #community #named #performance
SDREGION: Fast Spotting of Changing Communities in Biological Networks (SWHW, CP, MK, CF, IJ), pp. 867–875.
KDDKDD-2018-YangWZL0 #classification #multi
Complex Object Classification: A Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport (YY, YFW, DCZ, ZBL, YJ0), pp. 2594–2603.
KDDKDD-2018-YasarC
An Iterative Global Structure-Assisted Labeled Network Aligner (AY, ÜVÇ), pp. 2614–2623.
KDDKDD-2018-YingHCEHL #graph #recommendation
Graph Convolutional Neural Networks for Web-Scale Recommender Systems (RY, RH, KC, PE, WLH, JL), pp. 974–983.
KDDKDD-2018-YiZWLZ #distributed #predict #quality
Deep Distributed Fusion Network for Air Quality Prediction (XY, JZ, ZW, TL, YZ0), pp. 965–973.
KDDKDD-2018-YuCAZCW #approach #detection #flexibility #named
NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks (WY, WC, CCA, KZ0, HC, WW0), pp. 2672–2681.
KDDKDD-2018-YuZCASZCW #learning
Learning Deep Network Representations with Adversarially Regularized Autoencoders (WY, CZ, WC, CCA, DS, BZ, HC, WW0), pp. 2663–2671.
KDDKDD-2018-ZhangCWPY0 #proximity
Arbitrary-Order Proximity Preserved Network Embedding (ZZ, PC0, XW0, JP, XY, WZ0), pp. 2778–2786.
KDDKDD-2018-ZhouHYF #named #representation #self
SPARC: Self-Paced Network Representation for Few-Shot Rare Category Characterization (DZ, JH, HY, WF), pp. 2807–2816.
KDDKDD-2018-ZhouZSFZMYJLG #predict
Deep Interest Network for Click-Through Rate Prediction (GZ, XZ, CS, YF, HZ, XM, YY, JJ, HL, KG), pp. 1059–1068.
KDDKDD-2018-ZhuCW0
Deep Variational Network Embedding in Wasserstein Space (DZ, PC0, DW, WZ0), pp. 2827–2836.
KDDKDD-2018-ZugnerAG #graph
Adversarial Attacks on Neural Networks for Graph Data (DZ, AA, SG), pp. 2847–2856.
KDDKDD-2018-ZuoLLGHW
Embedding Temporal Network via Neighborhood Formation (YZ, GL, HL, JG, XH, JW), pp. 2857–2866.
ICMTICMT-2018-TomaszekLWS #model transformation
Virtual Network Embedding: Reducing the Search Space by Model Transformation Techniques (ST, EL, LW, AS), pp. 59–75.
OnwardOnward-2018-BasmanLC #design
The open authorial principle: supporting networks of authors in creating externalisable designs (AB, CHL, CBDC), pp. 29–43.
PLDIPLDI-2018-GehrMTVWV #named #probability
Bayonet: probabilistic inference for networks (TG, SM, PT, LV, PW, MTV), pp. 586–602.
PLDIPLDI-2018-LinWCLDW #architecture #manycore
Mapping spiking neural networks onto a manycore neuromorphic architecture (CKL, AW, GNC, THL, MD, HW), pp. 78–89.
SASSAS-2018-AlpernasMPSSSV #abstract interpretation
Abstract Interpretation of Stateful Networks (KA, RM, AP, MS, SS, SS, YV), pp. 86–106.
ASEASE-2018-QuLCJCHZ #2d #fault #named #predict #using
node2defect: using network embedding to improve software defect prediction (YQ, TL0, JC, YJ, DC, AH, QZ), pp. 844–849.
ASEASE-2018-SunWRHKK #testing
Concolic testing for deep neural networks (YS, MW, WR, XH0, MK, DK), pp. 109–119.
ESEC-FSEESEC-FSE-2018-Gusmanov #modelling #on the #reliability
On the adoption of neural networks in modeling software reliability (KG), pp. 962–964.
ESEC-FSEESEC-FSE-2018-LeB0 #mining #named #specification #using
DSM: a specification mining tool using recurrent neural network based language model (TDBL, LB, DL0), pp. 896–899.
ESEC-FSEESEC-FSE-2018-MaLLZG #analysis #automation #debugging #difference #named
MODE: automated neural network model debugging via state differential analysis and input selection (SM, YL, WCL, XZ, AG), pp. 175–186.
ASPLOSASPLOS-2018-BestaHYAMH #energy #performance #scalability
Slim NoC: A Low-Diameter On-Chip Network Topology for High Energy Efficiency and Scalability (MB, SMH, SY, RA, OM, TH), pp. 43–55.
ASPLOSASPLOS-2018-CaiRLDWQPW #hardware #named
VIBNN: Hardware Acceleration of Bayesian Neural Networks (RC, AR, NL0, CD, LW, XQ, MP, YW), pp. 476–488.
ASPLOSASPLOS-2018-JiZC0 #compilation #hardware
Bridge the Gap between Neural Networks and Neuromorphic Hardware with a Neural Network Compiler (YJ0, YZ, WC, YX0), pp. 448–460.
ASPLOSASPLOS-2018-MarkuzeSMT #named
DAMN: Overhead-Free IOMMU Protection for Networking (AM, IS, AM0, DT), pp. 301–315.
CASECASE-2018-CaoWLG #fault #generative
Application of Generative Adversarial Networks for Intelligent Fault Diagnosis (SC, LW, XL, LG0), pp. 711–715.
CASECASE-2018-KebriaKNNH #adaptation #nondeterminism
Neural Network Adaptive Control of Teleoperation Systems with Uncertainties and Time-Varying Delay (PMK, AK, SN, ZN, SJH), pp. 252–257.
CASECASE-2018-OriveABM #architecture #industrial #monitoring #resource management
Passive Network State Monitoring for Dynamic Resource Management in Industry 4.0 Fog Architectures (AO, AA, JB, MM), pp. 1414–1419.
CASECASE-2018-PetittiPCMSCN #approach #distributed #mobile
A Distributed Map Building Approach for Mobile Robotic Networks (AP, DDP, RC, AM, ES, AC, DN), pp. 116–121.
CASECASE-2018-SpenrathP #heuristic #random #using
Using Neural Networks for Heuristic Grasp Planning in Random Bin Picking (FS, AP), pp. 258–263.
CASECASE-2018-YangGWD #cyber-physical #distributed #energy #multi
A Multi-layered Distributed Cloud Network for Cyber-Physical Energy System (LY, XG, JW, SD), pp. 402–407.
CASECASE-2018-ZhangSRF #distributed #navigation
An autonomous robotic system for intralogistics assisted by distributed smart camera network for navigation (XZ, MS, SR, JF), pp. 1224–1229.
ESOPESOP-2018-BatzKKM #how #program analysis
How long, O Bayesian network, will I sample thee? - A program analysis perspective on expected sampling times (KB, BLK, JPK, CM), pp. 186–213.
FASEFASE-2018-GioulekasPKBP #parallel #process #streaming
A Process Network Model for Reactive Streaming Software with Deterministic Task Parallelism (FG, PP, PK, SB, PP), pp. 94–110.
ICSTICST-2018-JiLCPZ0YL #behaviour #nondeterminism #search-based #testing
Uncovering Unknown System Behaviors in Uncertain Networks with Model and Search-Based Testing (RJ, ZL, SC, MP, TZ0, SA0, TY0, XL), pp. 204–214.
ICTSSICTSS-2018-PrasetyaT #specification
Neural Networks as Artificial Specifications (ISWBP, MAT), pp. 135–141.
JCDLJCDL-2017-SikdarMGM #case study #energy #interactive #physics
Influence of Reviewer Interaction Network on Long-Term Citations: A Case Study of the Scientific Peer-Review System of the Journal of High Energy Physics (SS, MM, NG, AM0), pp. 179–188.
EDMEDM-2017-CaiEDPGS #analysis #chat #collaboration #learning #modelling #topic
Epistemic Network Analysis and Topic Modeling for Chat Data from Collaborative Learning Environment (ZC, BRE, ND, JWP, ACG, DWS).
EDMEDM-2017-GuoCC #approach #student
A Neural Network Approach to Estimate Student Skill Mastery in Cognitive Diagnostic Assessments (QG, MC, YC).
EDMEDM-2017-KuangCHN #analysis #framework #platform #social #topic
A Topic Model and Social Network Analysis of a School Blogging Platform (XK, HSC, BH, GN).
EDMEDM-2017-MichalenkoLB #feedback #memory management #personalisation #using
Personalized Feedback for Open-Response Mathematical Questions using Long Short-Term Memory Networks (JJM, ASL, RGB).
EDMEDM-2017-TatoND #automation #detection #reasoning
Convolutional Neural Network for Automatic Detection of Sociomoral Reasoning Level (AANT, RN, AD).
EDMEDM-2017-YasudaNKKH #automation #using
Automatic Scoring Method for Descriptive Test Using Recurrent Neural Network (KY, IN, HK, HK, MH).
EDMEDM-2017-ZhangS #interactive #modelling #scalability
Modeling Network Dynamics of MOOC Discussion Interactions at Scale (JZ, MS).
EDMEDM-2017-ZhaoH #education
Estimating Individual Treatment Effect from Educational Studies with Residual Counterfactual Networks (SZ, NTH).
ICPCICPC-2017-MostafaRW #android #behaviour #maintenance #named
NetDroid: summarizing network behavior of Android apps for network code maintenance (SM, RR, XW), pp. 165–175.
ICSMEICSME-2017-HanS #distance #evaluation #metric
Mean Average Distance to Resolver: An Evaluation Metric for Ticket Routing in Expert Network (JH, AS), pp. 594–602.
MSRMSR-2017-KikasGDP #dependence #evolution
Structure and evolution of package dependency networks (RK, GG, MD, DP), pp. 102–112.
IFM-2017-KovalovLGL #smt #using
Task-Node Mapping in an Arbitrary Computer Network Using SMT Solver (AK, EL, AG, DL), pp. 177–191.
SEFMSEFM-2017-WiikB #automation #data flow #specification #verification
Specification and Automated Verification of Dynamic Dataflow Networks (JW, PB), pp. 136–151.
HaskellHaskell-2017-DawsonGG #composition #monad
Composable network stacks and remote monads (JD, MG, AG), pp. 86–97.
CoGCIG-2017-AckerLB #automaton #simulation
Cellular automata simulation on FPGA for training neural networks with virtual world imagery (OVA, OL, GB), pp. 304–305.
CoGCIG-2017-HorsleyL #automation #generative
Building an automatic sprite generator with deep convolutional generative adversarial networks (LH, DPL), pp. 134–141.
CoGCIG-2017-NguyenRGM #automation #learning
Automated learning of hierarchical task networks for controlling minecraft agents (CN, NR, SG, HMA), pp. 226–231.
CoGCIG-2017-YoonK #game studies #visual notation
Deep Q networks for visual fighting game AI (SY, KJK), pp. 306–308.
FDGFDG-2017-TengB #approach #generative #semantics
A semantic approach to patch-based procedural generation of urban road networks (ET, RB), p. 10.
ICGJICGJ-2017-PirkerKG #analysis #game studies #social
Social network analysis of the global game jam network (JP, FK, CG), pp. 10–14.
CIKMCIKM-2017-ChavaryEL #mining #using
Summarizing Significant Changes in Network Traffic Using Contrast Pattern Mining (EAC, SME, CL), pp. 2015–2018.
CIKMCIKM-2017-ChengZZKZW #classification #sentiment
Aspect-level Sentiment Classification with HEAT (HiErarchical ATtention) Network (JC, SZ, JZ, IK, XZ0, HW0), pp. 97–106.
CIKMCIKM-2017-ChenXLDTCP #framework #named
HotSpots: Failure Cascades on Heterogeneous Critical Infrastructure Networks (LC, XX, SL, SD, AGT, SC, BAP), pp. 1599–1607.
CIKMCIKM-2017-ChenYSGHY #scalability
Community-Based Network Alignment for Large Attributed Network (ZC0, XY, BS, JG, XH, WSY), pp. 587–596.
CIKMCIKM-2017-CuiLZZ #analysis
Text Coherence Analysis Based on Deep Neural Network (BC, YL, YZ, ZZ), pp. 2027–2030.
CIKMCIKM-2017-DingZLTCZ #named #personalisation #ranking #recommendation
BayDNN: Friend Recommendation with Bayesian Personalized Ranking Deep Neural Network (DD, MZ0, SYL, JT0, XC, ZHZ), pp. 1479–1488.
CIKMCIKM-2017-FangYMG #scheduling
QoS-Aware Scheduling of Heterogeneous Servers for Inference in Deep Neural Networks (ZF, TY, OJM, RKG0), pp. 2067–2070.
CIKMCIKM-2017-FreitasTCX #agile #analysis
Rapid Analysis of Network Connectivity (SF, HT, NC, YX), pp. 2463–2466.
CIKMCIKM-2017-FuLL #learning #named #representation
HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning (TYF, WCL, ZL), pp. 1797–1806.
CIKMCIKM-2017-GalimbertiBG #composition #multi
Core Decomposition and Densest Subgraph in Multilayer Networks (EG, FB, FG), pp. 1807–1816.
CIKMCIKM-2017-GarciaM #energy #using
Inferring Appliance Energy Usage from Smart Meters using Fully Convolutional Encoder Decoder Networks (FCCG, EQBM), pp. 2075–2078.
CIKMCIKM-2017-Gupta0M #personalisation #ranking
Interest Diffusion in Heterogeneous Information Network for Personalized Item Ranking (MG, PK0, RM), pp. 2087–2090.
CIKMCIKM-2017-HoangL #clustering #mining #performance
Highly Efficient Mining of Overlapping Clusters in Signed Weighted Networks (TAH, EPL), pp. 869–878.
CIKMCIKM-2017-HuPJL #classification #graph
Graph Ladder Networks for Network Classification (RH, SP, JJ0, GL), pp. 2103–2106.
CIKMCIKM-2017-JinZ #modelling #social
Emotions in Social Networks: Distributions, Patterns, and Models (SJ, RZ), pp. 1907–1916.
CIKMCIKM-2017-KhuranaASVS #hybrid
Hybrid BiLSTM-Siamese network for FAQ Assistance (PK, PA, GMS, LV, AS0), pp. 537–545.
CIKMCIKM-2017-LiDHTCL #learning
Attributed Network Embedding for Learning in a Dynamic Environment (JL, HD, XH, JT, YC, HL0), pp. 387–396.
CIKMCIKM-2017-LyuZZ #quality #similarity
Enhancing the Network Embedding Quality with Structural Similarity (TL, YZ, YZ), pp. 147–156.
CIKMCIKM-2017-MalmiGT #approach
Active Network Alignment: A Matching-Based Approach (EM, AG, ET), pp. 1687–1696.
CIKMCIKM-2017-MumtazW #identification
Identifying Top-K Influential Nodes in Networks (SM, XW), pp. 2219–2222.
CIKMCIKM-2017-NicosiaM #hybrid
Accurate Sentence Matching with Hybrid Siamese Networks (MN, AM), pp. 2235–2238.
CIKMCIKM-2017-ParkLC #recommendation
Deep Neural Networks for News Recommendations (KP, JL, JC), pp. 2255–2258.
CIKMCIKM-2017-PeiYSZBT #recommendation
Interacting Attention-gated Recurrent Networks for Recommendation (WP, JY0, ZS, JZ0, AB, DMJT), pp. 1459–1468.
CIKMCIKM-2017-QuTSR00 #collaboration #framework #learning #multi #representation
An Attention-based Collaboration Framework for Multi-View Network Representation Learning (MQ, JT0, JS, XR, MZ0, JH0), pp. 1767–1776.
CIKMCIKM-2017-RaoTHJL
Talking to Your TV: Context-Aware Voice Search with Hierarchical Recurrent Neural Networks (JR, FT, HH, OJ, JL), pp. 557–566.
CIKMCIKM-2017-Saleem0CXP #named #social
IMaxer: A Unified System for Evaluating Influence Maximization in Location-based Social Networks (MAS, RK0, TC, XX, TBP), pp. 2523–2526.
CIKMCIKM-2017-SaravanouKVKG
Revealing the Hidden Links in Content Networks: An Application to Event Discovery (AS, IK, GV, VK, DG), pp. 2283–2286.
CIKMCIKM-2017-SathanurCJP #graph #simulation
When Labels Fall Short: Property Graph Simulation via Blending of Network Structure and Vertex Attributes (AVS, SC, CAJ, SP), pp. 2287–2290.
CIKMCIKM-2017-TayTH #analysis #memory management #sentiment
Dyadic Memory Networks for Aspect-based Sentiment Analysis (YT, LAT, SCH), pp. 107–116.
CIKMCIKM-2017-TayTPH #graph #multi #predict
Multi-Task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs (YT, LAT, MCP, SCH), pp. 1029–1038.
CIKMCIKM-2017-TengLW #detection #learning #multi #using
Anomaly Detection in Dynamic Networks using Multi-view Time-Series Hypersphere Learning (XT, YRL, XW), pp. 827–836.
CIKMCIKM-2017-ThonetCBP #modelling #social #topic
Users Are Known by the Company They Keep: Topic Models for Viewpoint Discovery in Social Networks (TT, GC, MB, KPS), pp. 87–96.
CIKMCIKM-2017-TianC #interactive #visualisation
Visualizing Deep Neural Networks with Interaction of Super-pixels (ST, YC), pp. 2327–2330.
CIKMCIKM-2017-WangATL
Attributed Signed Network Embedding (SW, CCA, JT, HL0), pp. 137–146.
CIKMCIKM-2017-WangDYT #comprehension #mobile #predict #social
Understanding and Predicting Weight Loss with Mobile Social Networking Data (ZW, TD, DY, JT), pp. 1269–1278.
CIKMCIKM-2017-WangSLSZ0
Distant Meta-Path Similarities for Text-Based Heterogeneous Information Networks (CW, YS, HL, YS, MZ0, JH0), pp. 1629–1638.
CIKMCIKM-2017-WuUBG #detection
Conflict of Interest Declaration and Detection System in Heterogeneous Networks (SW, LHU, SSB, WG), pp. 2383–2386.
CIKMCIKM-2017-XiangJ #learning #multimodal
Common-Specific Multimodal Learning for Deep Belief Network (CX, XJ), pp. 2387–2390.
CIKMCIKM-2017-XuM #analysis #multimodal #named #semantics #sentiment
MultiSentiNet: A Deep Semantic Network for Multimodal Sentiment Analysis (NX, WM), pp. 2399–2402.
CIKMCIKM-2017-XuWXQ #classification #graph #recursion
Attentive Graph-based Recursive Neural Network for Collective Vertex Classification (QX, QW, CX, LQ), pp. 2403–2406.
CIKMCIKM-2017-YangWLZL #graph
From Properties to Links: Deep Network Embedding on Incomplete Graphs (DY, SW, CL, XZ0, ZL), pp. 367–376.
CIKMCIKM-2017-YuanWLL #detection
Spectrum-based Deep Neural Networks for Fraud Detection (SY, XW, JL, AL), pp. 2419–2422.
CIKMCIKM-2017-ZhangH #ambiguity #graph #using
Name Disambiguation in Anonymized Graphs using Network Embedding (BZ, MAH), pp. 1239–1248.
ECIRECIR-2017-AletrasM #image #topic #using
Labeling Topics with Images Using a Neural Network (NA, AM), pp. 500–505.
ECIRECIR-2017-Anand0P #detection #exclamation #what
We Used Neural Networks to Detect Clickbaits: You Won't Believe What Happened Next! (AA, TC0, NP), pp. 541–547.
ECIRECIR-2017-ClosBWC #predict #social
Predicting Emotional Reaction in Social Networks (JC, AB, NW, GC), pp. 527–533.
ECIRECIR-2017-HuangHC #detection
Irony Detection with Attentive Recurrent Neural Networks (YHH, HHH, HHC), pp. 534–540.
ECIRECIR-2017-ManotumruksaMO #matrix #predict #rating #social #word
Matrix Factorisation with Word Embeddings for Rating Prediction on Location-Based Social Networks (JM, CM, IO), pp. 647–654.
ICMLICML-2017-AmosK #named #optimisation
OptNet: Differentiable Optimization as a Layer in Neural Networks (BA, JZK), pp. 136–145.
ICMLICML-2017-AmosXK
Input Convex Neural Networks (BA, LX, JZK), pp. 146–155.
ICMLICML-2017-ArjovskyCB #generative
Wasserstein Generative Adversarial Networks (MA, SC, LB), pp. 214–223.
ICMLICML-2017-ArpitJBKBKMFCBL
A Closer Look at Memorization in Deep Networks (DA, SJ, NB, DK, EB, MSK, TM, AF, ACC, YB, SLJ), pp. 233–242.
ICMLICML-2017-BalduzziMB #approximate #convergence
Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks (DB, BM, TBY), pp. 351–360.
ICMLICML-2017-BelangerYM #energy #learning #predict
End-to-End Learning for Structured Prediction Energy Networks (DB, BY, AM), pp. 429–439.
ICMLICML-2017-BolukbasiWDS #adaptation #performance
Adaptive Neural Networks for Efficient Inference (TB, JW0, OD, VS), pp. 527–536.
ICMLICML-2017-ChoB #named
MEC: Memory-efficient Convolution for Deep Neural Network (MC, DB), pp. 815–824.
ICMLICML-2017-CisseBGDU #robust
Parseval Networks: Improving Robustness to Adversarial Examples (MC, PB, EG, YND, NU), pp. 854–863.
ICMLICML-2017-CortesGKMY #adaptation #learning #named
AdaNet: Adaptive Structural Learning of Artificial Neural Networks (CC, XG, VK, MM, SY), pp. 874–883.
ICMLICML-2017-DauphinFAG #modelling
Language Modeling with Gated Convolutional Networks (YND, AF, MA, DG), pp. 933–941.
ICMLICML-2017-FeldmanOR #graph #summary
Coresets for Vector Summarization with Applications to Network Graphs (DF, SO, DR), pp. 1117–1125.
ICMLICML-2017-FinnAL #adaptation #performance
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks (CF, PA, SL), pp. 1126–1135.
ICMLICML-2017-FoersterGSCS #architecture
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability (JNF, JG, JSD, JC, DS), pp. 1136–1145.
ICMLICML-2017-GaoFC #learning
Local-to-Global Bayesian Network Structure Learning (TG, KPF, MC), pp. 1193–1202.
ICMLICML-2017-GravesBMMK #automation #education #learning
Automated Curriculum Learning for Neural Networks (AG, MGB, JM, RM, KK), pp. 1311–1320.
ICMLICML-2017-GuoPSW #on the
On Calibration of Modern Neural Networks (CG, GP, YS0, KQW), pp. 1321–1330.
ICMLICML-2017-GygliNA
Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs (MG, MN0, AA), pp. 1341–1351.
ICMLICML-2017-HongHZ #algorithm #distributed #learning #named #optimisation #performance
Prox-PDA: The Proximal Primal-Dual Algorithm for Fast Distributed Nonconvex Optimization and Learning Over Networks (MH, DH, MMZ), pp. 1529–1538.
ICMLICML-2017-HuQ #memory management
State-Frequency Memory Recurrent Neural Networks (HH, GJQ), pp. 1568–1577.
ICMLICML-2017-JingSDPSLTS #performance
Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs (LJ, YS, TD, JP, SAS, YL, MT, MS), pp. 1733–1741.
ICMLICML-2017-KalchbrennerOSD #video
Video Pixel Networks (NK, AvdO, KS, ID, OV, AG, KK), pp. 1771–1779.
ICMLICML-2017-KanskySMELLDSPG #generative #physics
Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics (KK, TS, DAM, ME, MLG, XL, ND, SS, DSP, DG), pp. 1809–1818.
ICMLICML-2017-KimCKLK #generative #learning
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks (TK, MC, HK, JKL, JK), pp. 1857–1865.
ICMLICML-2017-KimPKH #learning #named #parallel #parametricity #reduction #semantics
SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization (JK, YP, GK, SJH), pp. 1866–1874.
ICMLICML-2017-LiG
Dropout Inference in Bayesian Neural Networks with Alpha-divergences (YL, YG), pp. 2052–2061.
ICMLICML-2017-LivniCG #infinity #kernel #learning
Learning Infinite Layer Networks Without the Kernel Trick (RL, DC, AG), pp. 2198–2207.
ICMLICML-2017-LongZ0J #adaptation #learning
Deep Transfer Learning with Joint Adaptation Networks (ML, HZ, JW0, MIJ), pp. 2208–2217.
ICMLICML-2017-LouizosW #multi #normalisation
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks (CL, MW), pp. 2218–2227.
ICMLICML-2017-Luo #architecture #learning
Learning Deep Architectures via Generalized Whitened Neural Networks (PL0), pp. 2238–2246.
ICMLICML-2017-MaystreG17a #identification #named
ChoiceRank: Identifying Preferences from Node Traffic in Networks (LM, MG), pp. 2354–2362.
ICMLICML-2017-McGillP #how
Deciding How to Decide: Dynamic Routing in Artificial Neural Networks (MM, PP), pp. 2363–2372.
ICMLICML-2017-MeschederNG #generative
Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks (LMM, SN, AG), pp. 2391–2400.
ICMLICML-2017-MhammediHRB #orthogonal #performance #using
Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using Householder Reflections (ZM, ADH, AR, JB0), pp. 2401–2409.
ICMLICML-2017-MolchanovAV
Variational Dropout Sparsifies Deep Neural Networks (DM, AA, DPV), pp. 2498–2507.
ICMLICML-2017-MunkhdalaiY
Meta Networks (TM, HY0), pp. 2554–2563.
ICMLICML-2017-NeilLDL
Delta Networks for Optimized Recurrent Network Computation (DN, JL, TD, SCL), pp. 2584–2593.
ICMLICML-2017-NguyenH
The Loss Surface of Deep and Wide Neural Networks (QN0, MH0), pp. 2603–2612.
ICMLICML-2017-PenningtonB #geometry #matrix #random
Geometry of Neural Network Loss Surfaces via Random Matrix Theory (JP, YB), pp. 2798–2806.
ICMLICML-2017-RaghuPKGS #on the #power of
On the Expressive Power of Deep Neural Networks (MR, BP, JMK, SG, JSD), pp. 2847–2854.
ICMLICML-2017-RitterBSB #bias #case study
Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study (SR, DGTB, AS, MMB), pp. 2940–2949.
ICMLICML-2017-SafranS #approximate #trade-off
Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks (IS, OS), pp. 2979–2987.
ICMLICML-2017-SakrKS #precise
Analytical Guarantees on Numerical Precision of Deep Neural Networks (CS, YK0, NRS), pp. 3007–3016.
ICMLICML-2017-ScamanBBLM #algorithm #distributed #optimisation
Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks (KS, FRB, SB, YTL, LM), pp. 3027–3036.
ICMLICML-2017-SundararajanTY #axiom
Axiomatic Attribution for Deep Networks (MS, AT, QY), pp. 3319–3328.
ICMLICML-2017-Telgarsky
Neural Networks and Rational Functions (MT), pp. 3387–3393.
ICMLICML-2017-Tian #analysis #convergence
An Analytical Formula of Population Gradient for two-layered ReLU network and its Applications in Convergence and Critical Point Analysis (YT), pp. 3404–3413.
ICMLICML-2017-TompsonSSP #simulation
Accelerating Eulerian Fluid Simulation With Convolutional Networks (JT, KS, PS, KP), pp. 3424–3433.
ICMLICML-2017-VezhnevetsOSHJS #learning
FeUdal Networks for Hierarchical Reinforcement Learning (ASV, SO, TS, NH, MJ, DS, KK), pp. 3540–3549.
ICMLICML-2017-VorontsovTKP #dependence #learning #on the #orthogonal
On orthogonality and learning recurrent networks with long term dependencies (EV, CT, SK, CP), pp. 3570–3578.
ICMLICML-2017-YangKT #classification #video
Tensor-Train Recurrent Neural Networks for Video Classification (YY, DK, VT), pp. 3891–3900.
ICMLICML-2017-YoonH
Combined Group and Exclusive Sparsity for Deep Neural Networks (JY, SJH), pp. 3958–3966.
ICMLICML-2017-ZhangLW
Convexified Convolutional Neural Networks (YZ0, PL, MJW), pp. 4044–4053.
ICMLICML-2017-ZhangZZHZ #distributed #learning #online
Projection-free Distributed Online Learning in Networks (WZ0, PZ, WZ0, SCHH, TZ), pp. 4054–4062.
ICMLICML-2017-ZhaoLW0TY #matrix #rank
Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank (LZ, SL, YW, ZL0, JT0, BY0), pp. 4082–4090.
ICMLICML-2017-ZhongS0BD
Recovery Guarantees for One-hidden-layer Neural Networks (KZ, ZS, PJ0, PLB, ISD), pp. 4140–4149.
ICMLICML-2017-ZillySKS
Recurrent Highway Networks (JGZ, RKS, JK, JS), pp. 4189–4198.
KDDKDD-2017-AlbertKG #identification #scalability #using
Using Convolutional Networks and Satellite Imagery to Identify Patterns in Urban Environments at a Large Scale (AA, JK, MCG), pp. 1357–1366.
KDDKDD-2017-AvinLNP #bound
Improved Degree Bounds and Full Spectrum Power Laws in Preferential Attachment Networks (CA, ZL, YN, DP), pp. 45–53.
KDDKDD-2017-BaiWT0D
Unsupervised Network Discovery for Brain Imaging Data (ZB, PBW, AET, FW0, ID), pp. 55–64.
KDDKDD-2017-BaytasXZWJZ #type system
Patient Subtyping via Time-Aware LSTM Networks (IMB, CX, XZ, FW0, AKJ0, JZ), pp. 65–74.
KDDKDD-2017-ChengLL #feature model #social
Unsupervised Feature Selection in Signed Social Networks (KC, JL, HL0), pp. 777–786.
KDDKDD-2017-DadkhahiM #detection #embedded #learning
Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices (HD, BMM), pp. 1773–1781.
KDDKDD-2017-DebGIPVYY #automation #learning #named #policy #predict
AESOP: Automatic Policy Learning for Predicting and Mitigating Network Service Impairments (SD, ZG, SI, SCP, SV, HY, JY), pp. 1783–1792.
KDDKDD-2017-DongCS #learning #named #representation #scalability
metapath2vec: Scalable Representation Learning for Heterogeneous Networks (YD, NVC, AS), pp. 135–144.
KDDKDD-2017-DongJXC #case study
Structural Diversity and Homophily: A Study Across More Than One Hundred Big Networks (YD, RAJ, JX, NVC), pp. 807–816.
KDDKDD-2017-EikmeierG
Revisiting Power-law Distributions in Spectra of Real World Networks (NE, DFG), pp. 817–826.
KDDKDD-2017-GuSG #co-evolution #evolution #migration #social
The Co-Evolution Model for Social Network Evolving and Opinion Migration (YG, YS, JG), pp. 175–184.
KDDKDD-2017-HallacPBL #visual notation
Network Inference via the Time-Varying Graphical Lasso (DH, YP, SPB, JL), pp. 205–213.
KDDKDD-2017-HouYSA #android #detection #named
HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network (SH, YY, YS, MA), pp. 1507–1515.
KDDKDD-2017-LiMGK #interactive
A Context-aware Attention Network for Interactive Question Answering (HL, MRM, YG, AK), pp. 927–935.
KDDKDD-2017-MaCZYSG #bidirectional #named #predict
Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks (FM, RC, JZ, QY, TS, JG0), pp. 1903–1911.
KDDKDD-2017-MottiniA #pointer #predict #using
Deep Choice Model Using Pointer Networks for Airline Itinerary Prediction (AM, RAA), pp. 1575–1583.
KDDKDD-2017-PanZLCHHZ #scalability
An Intelligent Customer Care Assistant System for Large-Scale Cellular Network Diagnosis (LP, JZ, PPCL, HC, CH, CH, KZ), pp. 1951–1959.
KDDKDD-2017-SaveskiPSDGXA #detection #random
Detecting Network Effects: Randomizing Over Randomized Experiments (MS, JPA, GSJ, WD, SG, YX, EMA), pp. 1027–1035.
KDDKDD-2017-Scholtes #multi #visual notation
When is a Network a Network?: Multi-Order Graphical Model Selection in Pathways and Temporal Networks (IS), pp. 1037–1046.
KDDKDD-2017-ShenHYSLC #on the #online #social
On Finding Socially Tenuous Groups for Online Social Networks (CYS, LHH, DNY, HHS, WCL, MSC), pp. 415–424.
KDDKDD-2017-ShiCZG0 #named #probability
PReP: Path-Based Relevance from a Probabilistic Perspective in Heterogeneous Information Networks (YS, PWC, HZ, HG, JH0), pp. 425–434.
KDDKDD-2017-SinghSGMC #evolution #modelling
Relay-Linking Models for Prominence and Obsolescence in Evolving Networks (MS0, RS, PG, AM0, SC), pp. 1077–1086.
KDDKDD-2017-WangGZOXLG #detection
Adversary Resistant Deep Neural Networks with an Application to Malware Detection (QW, WG, KZ, AGOI, XX, XL, CLG), pp. 1145–1153.
KDDKDD-2017-WangHCLYR #mining
Structural Deep Brain Network Mining (SW, LH0, BC, CTL, PSY, ABR), pp. 475–484.
KDDKDD-2017-YeZMPB #learning
Learning from Labeled and Unlabeled Vertices in Networks (WY0, LZ, DM, CP, CB), pp. 1265–1274.
KDDKDD-2017-YinCZ #comprehension #design #named #sequence
DeepProbe: Information Directed Sequence Understanding and Chatbot Design via Recurrent Neural Networks (ZY, KhC, RZ), pp. 2131–2139.
KDDKDD-2017-YouX0T #education #learning #multi
Learning from Multiple Teacher Networks (SY, CX0, CX0, DT), pp. 1285–1294.
KDDKDD-2017-ZhaoYLSL #recommendation
Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks (HZ, QY, JL, YS, DLL), pp. 635–644.
ECMFAECMFA-2017-KlugeSGSM #c #embedded #generative #modelling #named
cMoflon: Model-Driven Generation of Embedded C Code for Wireless Sensor Networks (RK, MS, DG, AS, MM), pp. 109–125.
PLDIPLDI-2017-BeckettMMPW #synthesis
Network configuration synthesis with abstract topologies (RB, RM, TDM, JP, DW), pp. 437–451.
POPLPOPL-2017-SmolkaKFK0 #probability #semantics
Cantor meets scott: semantic foundations for probabilistic networks (SS, PK0, NF, DK, AS0), pp. 557–571.
POPLPOPL-2017-SubramanianDA #multitenancy #named #overview
Genesis: synthesizing forwarding tables in multi-tenant networks (KS, LD, AA), pp. 572–585.
ASEASE-2017-Perez-SolerGLJ #modelling #social #towards
The rise of the (modelling) bots: towards assisted modelling via social networks (SPS, EG, JdL, FJ0), pp. 723–728.
ASEASE-2017-RafiqDRBYSLCPN #adaptation #learning #online #re-engineering #social
Learning to share: engineering adaptive decision-support for online social networks (YR, LD, AR, AKB, MY, AS, ML, GC, BAP, BN), pp. 280–285.
ESEC-FSEESEC-FSE-2017-DijkCHB #modelling #privacy #re-engineering
Model-driven software engineering in practice: privacy-enhanced filtering of network traffic (RvD, CC, JvdH, JvdB), pp. 860–865.
ESEC-FSEESEC-FSE-2017-HellendoornD #modelling #question #source code
Are deep neural networks the best choice for modeling source code? (VJH, PTD), pp. 763–773.
ICSE-2017-JoblinAHM #developer #empirical #metric
Classifying developers into core and peripheral: an empirical study on count and network metrics (MJ, SA, CH, WM), pp. 164–174.
ASPLOSASPLOS-2017-GaoPYHK #3d #memory management #named #performance #scalability
TETRIS: Scalable and Efficient Neural Network Acceleration with 3D Memory (MG, JP, XY, MH, CK), pp. 751–764.
ASPLOSASPLOS-2017-HuSL #towards
Towards “Full Containerization” in Containerized Network Function Virtualization (YH0, MS, TL0), pp. 467–481.
ASPLOSASPLOS-2017-LesokhinERSGLBA #fault
Page Fault Support for Network Controllers (IL, HE, SR, GS, SG, LL, MBY, NA, DT), pp. 449–466.
ASPLOSASPLOS-2017-RenLDQWLQY #named #probability #using
SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing (AR, ZL0, CD, QQ, YW, JL0, XQ, BY0), pp. 405–418.
CASECASE-2017-AskinH #automation #estimation
Automated lead time estimation for manufacturing networks with dynamic demand (RGA, GJH), pp. 994–999.
CASECASE-2017-CuiHD #collaboration #problem
4PL collaborative routing customization problem on the dynamic networks (YC, MH0, QD), pp. 1345–1349.
CASECASE-2017-CuiVZBB #adaptation #architecture #self
A software architecture supporting self-adaptation of wireless control networks (YC, RMV, XZ, JB, ESB), pp. 346–351.
CASECASE-2017-DingWL #approach #feedback #logic #matrix
Output feedback disturbance decoupling of boolean control networks: A logical matrix factorization approach (XD, SW, HL0), pp. 181–186.
CASECASE-2017-HuZY #evaluation #process #quality #research
Research on quality fluctuation evaluation based on state information network entropy in intelligent manufacturing process (SH, LZ0, YY), pp. 446–450.
CASECASE-2017-LiLPS #capacity #distributed #energy
Optimal placement and capacity allocation of distributed energy storage devices in distribution networks (WL, CL, XP, JS0), pp. 1403–1407.
CASECASE-2017-LiuJZ #metric #optimisation
An evacuation guider location optimization method based on road network centrality measures (ZL, QSJ, HZ), pp. 838–843.
CASECASE-2017-LiuLQLD #estimation
State estimation for BAM neural networks with mixed time delays (GL, CL, JQ, XL, JD), pp. 1506–1509.
CASECASE-2017-TianJW #sequence #social
Opinion containment in social networks over issue sequences (YT, GJ, LW0), pp. 1374–1379.
CASECASE-2017-Wang
Regulation by competing: A hidden layer of gene regulatory networks (XW), p. 96.
CASECASE-2017-WangWQL #constraints #metaprogramming #social
From micro to macro: Propagated constraints in social networks (CW, YW, TQ, HL), pp. 1534–1539.
CASECASE-2017-WuXS #protocol #trust
Trust-based protocol for securing routing in opportunistic networks (XW, JX, JS), pp. 434–439.
CASECASE-2017-YangLXC #algebra #logic
Controllability of dynamic-algebraic mix-valued logical control networks (LY, BL, RX, JC), pp. 171–176.
CASECASE-2017-ZhangJ #bias #distributed #metric
Target tracking over distributed sensor networks by polar measurements with time-varying bias (CZ, YJ), pp. 429–433.
CAVCAV-2017-HuangKWW #safety #verification
Safety Verification of Deep Neural Networks (XH0, MK, SW, MW), pp. 3–29.
CAVCAV-2017-KatzBDJK #named #performance #smt #verification
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks (GK, CWB, DLD, KJ, MJK), pp. 97–117.
CAVCAV-2017-CardelliCFKLPW #synthesis
Syntax-Guided Optimal Synthesis for Chemical Reaction Networks (LC, MC0, MF, MZK, LL, NP, MW), pp. 375–395.
CAVCAV-2017-McClurgHC #source code #synthesis
Synchronization Synthesis for Network Programs (JM, HH, PC), pp. 301–321.
QoSAQoSA-2016-PagliariMPT #adaptation #energy #information management #peer-to-peer
Energy-Aware Adaptive Techniques for Information Diffusion in Ungoverned Peer-to-Peer Networks (LP, RM, DPP, CT), pp. 96–105.
EDMEDM-2016-SharmaBGPD16a #education #multimodal #named #predict
LIVELINET: A Multimodal Deep Recurrent Neural Network to Predict Liveliness in Educational Videos (AS, AB, AG, SP, OD), pp. 215–222.
EDMEDM-2016-SharmaBGPD16a_ #education #multimodal #named #predict
LIVELINET: A Multimodal Deep Recurrent Neural Network to Predict Liveliness in Educational Videos (AS, AB, AG, SP, OD), pp. 215–222.
EDMEDM-2016-WilsonKHE #estimation
Back to the basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation (KHW, YK, BH, CE), pp. 539–544.
FMFM-2016-CimattiMS #automaton #hybrid
From Electrical Switched Networks to Hybrid Automata (AC, SM, MS), pp. 164–181.
AIIDEAIIDE-2016-SinghDHJPM #using
Recognizing Actions in Motion Trajectories Using Deep Neural Networks (KYS, NMD, CPH, MJ, KP, BM), pp. 211–217.
CHI-PLAYCHI-PLAY-2016-CollinsCL #exclamation #game studies #student
Say Cheese!: Games for Successful Academic and Student Networking (EIMC, ALC, FJL), pp. 105–115.
CHI-PLAYCHI-PLAY-2016-WellsCLMGS #data mining #mining
Mining for Gold (and Platinum): PlayStation Network Data Mining (LW, AJCS, IJL, LM, BG, KdS), pp. 304–312.
CoGCIG-2016-SoemersW #game studies #reuse #video
Hierarchical Task Network Plan Reuse for video games (DJNJS, MHMW), pp. 1–8.
CoGCIG-2016-StanescuBHB #game studies #realtime #using
Evaluating real-time strategy game states using convolutional neural networks (MS, NAB, AH, MB), pp. 1–7.
DiGRADiGRA-FDG-2016-PaavilainenAK #game studies #overview #social
Review of Social Features in Social Network Games (JP, KA, HK).
CIKMCIKM-2016-0002AK #distance #query
Fully Dynamic Shortest-Path Distance Query Acceleration on Massive Networks (TH0, TA, KiK), pp. 1533–1542.
CIKMCIKM-2016-AnwarLV0 #evolution
Tracking the Evolution of Congestion in Dynamic Urban Road Networks (TA, CL, HLV, MSI0), pp. 2323–2328.
CIKMCIKM-2016-CaoY #benchmark #dataset #metric #modelling #named #platform #social
ASNets: A Benchmark Dataset of Aligned Social Networks for Cross-Platform User Modeling (XC, YY0), pp. 1881–1884.
CIKMCIKM-2016-ChenWPT #community #performance #using
Efficient Computation of Importance Based Communities in Web-Scale Networks Using a Single Machine (SC, RW, DP, AT), pp. 1553–1562.
CIKMCIKM-2016-ChenZH
Incorporate Group Information to Enhance Network Embedding (JC, QZ0, XH), pp. 1901–1904.
CIKMCIKM-2016-FanWL #analysis #metric #mobile #on the #optimisation
On Backup Battery Data in Base Stations of Mobile Networks: Measurement, Analysis, and Optimization (XF, FW0, JL), pp. 1513–1522.
CIKMCIKM-2016-JiangL
Forecasting Geo-sensor Data with Participatory Sensing Based on Dropout Neural Network (JYJ, CTL), pp. 2033–2036.
CIKMCIKM-2016-JiangYCZY #privacy #query #reachability
Privacy-Preserving Reachability Query Services for Massive Networks (JJ, PY, BC, ZZ, XY0), pp. 145–154.
CIKMCIKM-2016-KasneciG #linear #named
LICON: A Linear Weighting Scheme for the Contribution ofInput Variables in Deep Artificial Neural Networks (GK, TG), pp. 45–54.
CIKMCIKM-2016-LiKZH #classification #on the
On Transductive Classification in Heterogeneous Information Networks (XL, BK, YZ, ZH0), pp. 811–820.
CIKMCIKM-2016-LiTCELB #interactive #named #optimisation
TEAMOPT: Interactive Team Optimization in Big Networks (LL, HT, NC, KE, YRL, NB), pp. 2485–2487.
CIKMCIKM-2016-LuQZHG #approach #recommendation #social
Location-aware Friend Recommendation in Event-based Social Networks: A Bayesian Latent Factor Approach (YL, ZQ, CZ0, YH0, LG0), pp. 1957–1960.
CIKMCIKM-2016-ManshaKKA #identification #self #speech
A Self-Organizing Map for Identifying InfluentialCommunities in Speech-based Networks (SM, FK, AK, AA), pp. 1965–1968.
CIKMCIKM-2016-MaSCYKV #distributed #performance #query
Query Answering Efficiency in Expert Networks Under Decentralized Search (LM0, MS, DC, XY, SK, MV), pp. 2119–2124.
CIKMCIKM-2016-NegiC #predict #social
Link Prediction in Heterogeneous Social Networks (SN, SC), pp. 609–617.
CIKMCIKM-2016-RaoHL #estimation
Noise-Contrastive Estimation for Answer Selection with Deep Neural Networks (JR, HH, JJL), pp. 1913–1916.
CIKMCIKM-2016-RongZC #approach
A Model-Free Approach to Infer the Diffusion Network from Event Cascade (YR, QZ, HC), pp. 1653–1662.
CIKMCIKM-2016-RossiZ #multi #scalability
Leveraging Multiple GPUs and CPUs for Graphlet Counting in Large Networks (RAR, RZ0), pp. 1783–1792.
CIKMCIKM-2016-RuanSXTFLZ #using
Forecasting Seasonal Time Series Using Weighted Gradient RBF Network based Autoregressive Model (WR, QZS, PX, NKT0, NJGF, XL0, WEZ), pp. 2021–2024.
CIKMCIKM-2016-SatyaLLTZ #online #social
Uncovering Fake Likers in Online Social Networks (PRBS, KL, DL0, TT, J(Z), pp. 2365–2370.
CIKMCIKM-2016-SongHL #social
Targeted Influence Maximization in Social Networks (CS, WH, MLL), pp. 1683–1692.
CIKMCIKM-2016-TangCSTV0
BigNet 2016: First Workshop on Big Network Analytics (JT0, KC, ZS, HT, MV, YY0), pp. 2505–2506.
CIKMCIKM-2016-TanWX #approach #recommendation
A Neural Network Approach to Quote Recommendation in Writings (JT, XW0, JX), pp. 65–74.
CIKMCIKM-2016-UfimtsevSMB #comprehension #metric
Understanding Stability of Noisy Networks through Centrality Measures and Local Connections (VU, SS, AM0, SB), pp. 2347–2352.
CIKMCIKM-2016-ZengZMZW #clustering #predict
Exploiting Cluster-based Meta Paths for Link Prediction in Signed Networks (JZ, KZ0, XM, FZ, HW), pp. 1905–1908.
CIKMCIKM-2016-ZhangGWHH #predict #twitter
Retweet Prediction with Attention-based Deep Neural Network (QZ0, YG, JW, HH, XH), pp. 75–84.
CIKMCIKM-2016-ZhangTL #clustering #multi
Clustering Speed in Multi-lane Traffic Networks (BZ, GT, FL), pp. 2045–2048.
CIKMCIKM-2016-ZhangYZZ #classification #matrix
Collective Classification via Discriminative Matrix Factorization on Sparsely Labeled Networks (DZ, JY, XZ, CZ), pp. 1563–1572.
ICMLICML-2016-AllamanisPS #source code #summary
A Convolutional Attention Network for Extreme Summarization of Source Code (MA, HP, CAS), pp. 2091–2100.
ICMLICML-2016-AlmahairiBCZLC #capacity
Dynamic Capacity Networks (AA, NB, TC, YZ, HL, ACC), pp. 2549–2558.
ICMLICML-2016-ArjovskySB #evolution
Unitary Evolution Recurrent Neural Networks (MA, AS, YB), pp. 1120–1128.
ICMLICML-2016-ArpitZKG #normalisation #parametricity
Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks (DA, YZ, BUK, VG), pp. 1168–1176.
ICMLICML-2016-BalduzziG
Strongly-Typed Recurrent Neural Networks (DB, MG), pp. 1292–1300.
ICMLICML-2016-BelangerM #energy #predict
Structured Prediction Energy Networks (DB, AM), pp. 983–992.
ICMLICML-2016-BlondelIFU #algorithm #performance #polynomial
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms (MB, MI, AF, NU), pp. 850–858.
ICMLICML-2016-CohenS
Convolutional Rectifier Networks as Generalized Tensor Decompositions (NC, AS), pp. 955–963.
ICMLICML-2016-CohenW
Group Equivariant Convolutional Networks (TC, MW), pp. 2990–2999.
ICMLICML-2016-CouilletWAS #approach #matrix #random
A Random Matrix Approach to Echo-State Neural Networks (RC, GW, HTA, HS), pp. 517–525.
ICMLICML-2016-DielemanFK #symmetry
Exploiting Cyclic Symmetry in Convolutional Neural Networks (SD, JDF, KK), pp. 1889–1898.
ICMLICML-2016-Gilad-BachrachD #named #throughput
CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy (RGB, ND, KL, KEL, MN, JW), pp. 201–210.
ICMLICML-2016-HenaffSL #orthogonal
Recurrent Orthogonal Networks and Long-Memory Tasks (MH, AS, YL), pp. 2034–2042.
ICMLICML-2016-KordaSL #clustering #distributed #linear
Distributed Clustering of Linear Bandits in Peer to Peer Networks (NK, BS, SL), pp. 1301–1309.
ICMLICML-2016-KumarIOIBGZPS #memory management #natural language
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing (AK, OI, PO, MI, JB0, IG, VZ, RP, RS), pp. 1378–1387.
ICMLICML-2016-LinTA #fixpoint
Fixed Point Quantization of Deep Convolutional Networks (DDL, SST, VSA), pp. 2849–2858.
ICMLICML-2016-LiOW #multi
Multi-Bias Non-linear Activation in Deep Neural Networks (HL, WO, XW0), pp. 221–229.
ICMLICML-2016-LiuSSF #learning #markov
Structure Learning of Partitioned Markov Networks (SL0, TS, MS, KF), pp. 439–448.
ICMLICML-2016-LiuWYY
Large-Margin Softmax Loss for Convolutional Neural Networks (WL, YW, ZY, MY0), pp. 507–516.
ICMLICML-2016-NiepertAK #graph #learning
Learning Convolutional Neural Networks for Graphs (MN, MA, KK), pp. 2014–2023.
ICMLICML-2016-OordKK
Pixel Recurrent Neural Networks (AvdO, NK, KK), pp. 1747–1756.
ICMLICML-2016-OswalCRRN #learning #similarity
Representational Similarity Learning with Application to Brain Networks (UO, CRC, MALR, TTR, RDN), pp. 1041–1049.
ICMLICML-2016-PaigeW #modelling #monte carlo #visual notation
Inference Networks for Sequential Monte Carlo in Graphical Models (BP, FDW), pp. 3040–3049.
ICMLICML-2016-PanS
Expressiveness of Rectifier Networks (XP, VS), pp. 2427–2435.
ICMLICML-2016-PezeshkiFBCB #architecture
Deconstructing the Ladder Network Architecture (MP, LF, PB, ACC, YB), pp. 2368–2376.
ICMLICML-2016-SafranS #on the #quality
On the Quality of the Initial Basin in Overspecified Neural Networks (IS, OS), pp. 774–782.
ICMLICML-2016-SantoroBBWL
Meta-Learning with Memory-Augmented Neural Networks (AS, SB, MB, DW, TPL), pp. 1842–1850.
ICMLICML-2016-ShangSAL #comprehension #linear
Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units (WS, KS, DA, HL), pp. 2217–2225.
ICMLICML-2016-SongGC #learning #sequence
Factored Temporal Sigmoid Belief Networks for Sequence Learning (JS, ZG, LC), pp. 1272–1281.
ICMLICML-2016-SongSZU
Training Deep Neural Networks via Direct Loss Minimization (YS, AGS, RSZ, RU), pp. 2169–2177.
ICMLICML-2016-TaylorBXSPG #approach #scalability
Training Neural Networks Without Gradients: A Scalable ADMM Approach (GT, RB, ZX0, BS, ABP, TG), pp. 2722–2731.
ICMLICML-2016-UlyanovLVL #image #synthesis
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images (DU, VL, AV, VSL), pp. 1349–1357.
ICMLICML-2016-WangMCBPRGUA #analysis #matrix
Analysis of Deep Neural Networks with Extended Data Jacobian Matrix (SW, ArM, RC, JAB, MP, MR, KG, GU, ÖA), pp. 718–726.
ICMLICML-2016-WangSHHLF #architecture #learning
Dueling Network Architectures for Deep Reinforcement Learning (ZW0, TS, MH, HvH, ML, NdF), pp. 1995–2003.
ICMLICML-2016-WeiWRC #morphism
Network Morphism (TW, CW, YR, CWC), pp. 564–572.
ICMLICML-2016-XiongMS #memory management #visual notation
Dynamic Memory Networks for Visual and Textual Question Answering (CX, SM, RS), pp. 2397–2406.
ICMLICML-2016-ZhangLJ #polynomial
L1-regularized Neural Networks are Improperly Learnable in Polynomial Time (YZ0, JDL, MIJ), pp. 993–1001.
ICMLICML-2016-ZhangLL #classification #image #scalability
Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification (YZ, KL, HL), pp. 612–621.
ICMLICML-2016-ZhaoAGA
Collapsed Variational Inference for Sum-Product Networks (HZ0, TA, GJG, BA), pp. 1310–1318.
ICPRICPR-2016-AntonyMOM #using
Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks (JA, KM, NEO, KM), pp. 1195–1200.
ICPRICPR-2016-BSSH #approach #classification #using
HEp-2 cell classification using artificial neural network approach (DB, KS, NH), pp. 84–89.
ICPRICPR-2016-CamgozHKB #3d #gesture #independence #recognition #using
Using Convolutional 3D Neural Networks for User-independent continuous gesture recognition (NCC, SH, OK, RB), pp. 49–54.
ICPRICPR-2016-CapuaNP #detection #social
Unsupervised cyber bullying detection in social networks (MDC, EDN, AP), pp. 432–437.
ICPRICPR-2016-ChaiLYLC #gesture #recognition #scalability
Two streams Recurrent Neural Networks for Large-Scale Continuous Gesture Recognition (XC, ZL, FY, ZL, XC), pp. 31–36.
ICPRICPR-2016-ChenQYJ #bidirectional
Face alignment with Cascaded Bidirectional LSTM Neural Networks (YC, JQ, JY0, ZJ), pp. 313–318.
ICPRICPR-2016-ChenZW #approach #learning #summary #video
Wireless capsule endoscopy video summarization: A learning approach based on Siamese neural network and support vector machine (JC, YZ, YW0), pp. 1303–1308.
ICPRICPR-2016-ChoiKPS #detection #multi
Multi-spectral pedestrian detection based on accumulated object proposal with fully convolutional networks (HC, SK, KP, KS), pp. 621–626.
ICPRICPR-2016-ChowdhuryBMKS #image #performance #retrieval #using
An efficient radiographic Image Retrieval system using Convolutional Neural Network (MC, SRB, RM, MKK, ÖS), pp. 3134–3139.
ICPRICPR-2016-CorniaBSC #multi #predict
A deep multi-level network for saliency prediction (MC, LB, GS0, RC), pp. 3488–3493.
ICPRICPR-2016-DuWZH #markov #recognition
Deep neural network based hidden Markov model for offline handwritten Chinese text recognition (JD, ZRW, JFZ, JSH), pp. 3428–3433.
ICPRICPR-2016-FengLXYM #traversal
Face hallucination by deep traversal network (ZXF, JHL, XX, DY, LM), pp. 3276–3281.
ICPRICPR-2016-GaoYGC #approach #constraints #using
Bayesian approach to learn Bayesian networks using data and constraints (XGG, YY, ZgG, DQC0), pp. 3667–3672.
ICPRICPR-2016-GhaderiA #learning
Selective unsupervised feature learning with Convolutional Neural Network (S-CNN) (AG, VA), pp. 2486–2490.
ICPRICPR-2016-GrantSZG #predict #visualisation
Predicting and visualizing psychological attributions with a deep neural network (EG, SS, MZ, MvG), pp. 1–6.
ICPRICPR-2016-GwonCK
Deep Sparse-coded Network (DSN) (YG, MC, HTK), pp. 2610–2615.
ICPRICPR-2016-HajduHBLEHT #algorithm #approximate #grid #using
Measuring regularity of network patterns by grid approximations using the LLL algorithm (AH, BH, RB, IL, GE, LH, RT), pp. 1524–1529.
ICPRICPR-2016-HasegawaH #classification #image #named #using
PLSNet: A simple network using Partial Least Squares regression for image classification (RH, KH), pp. 1601–1606.
ICPRICPR-2016-HuangY #3d #using
Point cloud labeling using 3D Convolutional Neural Network (JH0, SY), pp. 2670–2675.
ICPRICPR-2016-JhuangLT #3d #using #verification
Face verification with three-dimensional point cloud by using deep belief networks (DHJ, DTL, CHT), pp. 1430–1435.
ICPRICPR-2016-JiaSZY #classification
Deep convolutional neural network based HEp-2 cell classification (XJ, LS, XZ, SY), pp. 77–80.
ICPRICPR-2016-KabkabHC #on the #performance
On the size of Convolutional Neural Networks and generalization performance (MK, EMH, RC), pp. 3572–3577.
ICPRICPR-2016-KalraSRT #learning #using
Learning opposites using neural networks (SK, AS, SR, HRT), pp. 1213–1218.
ICPRICPR-2016-KimP #approach #detection #using
A shape preserving approach for salient object detection using convolutional neural networks (JK, VP), pp. 609–614.
ICPRICPR-2016-KimP16a #using
Discovering characteristic landmarks on ancient coins using convolutional networks (JK, VP), pp. 1595–1600.
ICPRICPR-2016-LiaoLL #classification #multi #robust #using
Skin disease classification versus skin lesion characterization: Achieving robust diagnosis using multi-label deep neural networks (HL, YL, JL), pp. 355–360.
ICPRICPR-2016-LiSZY #classification
HEp-2 specimen classification with fully convolutional network (YL, LS, XZ, SY), pp. 96–100.
ICPRICPR-2016-LittwinW #complexity #multi
Complexity of multiverse networks and their multilayer generalization (EL, LW), pp. 372–377.
ICPRICPR-2016-Liu16a #classification #learning #multi #scalability
Hierarchical learning for large multi-class network classification (LL), pp. 2307–2312.
ICPRICPR-2016-LiuGX #constraints #synthesis
Texture synthesis through convolutional neural networks and spectrum constraints (GL0, YG, GSX), pp. 3234–3239.
ICPRICPR-2016-Lobato-RiosHCT #comparison #linear #quality
Linear model optimizer vs Neural Networks: A comparison for improving the quality and saving of LED-Lighting control systems (VLR, VdRHC, JACO, JFMT), pp. 2664–2669.
ICPRICPR-2016-LyuSZB #image #multi
Distinguishing text/non-text natural images with Multi-Dimensional Recurrent Neural Networks (PL, BS, CZ, XB), pp. 3981–3986.
ICPRICPR-2016-McCaneS #performance
Deep networks are efficient for circular manifolds (BM, LS), pp. 3464–3469.
ICPRICPR-2016-MeiDSB #identification
Scene text script identification with Convolutional Recurrent Neural Networks (JM, LD, BS, XB), pp. 4053–4058.
ICPRICPR-2016-MelekhovKR #image
Siamese network features for image matching (IM, JK, ER), pp. 378–383.
ICPRICPR-2016-MinelloTH #evolution #quantum
Quantum thermodynamics of time evolving networks (GM, AT, ERH), pp. 1536–1541.
ICPRICPR-2016-MurguiaRA #adaptation #architecture #dataset #evaluation #modelling #parallel
Evaluation of the background modeling method Auto-Adaptive Parallel Neural Network Architecture in the SBMnet dataset (MICM, JARQ, GRA), pp. 137–142.
ICPRICPR-2016-NairKNG #documentation #segmentation #using
Segmentation of highly unstructured handwritten documents using a neural network technique (RRN, BUK, IN, VG), pp. 1291–1296.
ICPRICPR-2016-NieZJ #data transformation #representation
Latent regression Bayesian network for data representation (SN, YZ, QJ), pp. 3494–3499.
ICPRICPR-2016-NookaCVSP #adaptation #classification
Adaptive hierarchical classification networks (SPN, SC, KV, SS, RWP), pp. 3578–3583.
ICPRICPR-2016-PandaDR #multi #summary #video
Video summarization in a multi-view camera network (RP, AD, AKRC), pp. 2971–2976.
ICPRICPR-2016-PangN #3d #detection #multi
3D point cloud object detection with multi-view convolutional neural network (GP, UN), pp. 585–590.
ICPRICPR-2016-PengRP #learning #recognition #using
Learning face recognition from limited training data using deep neural networks (XP, NKR, SP), pp. 1442–1447.
ICPRICPR-2016-PengZ
Mutual information-based RBM neural networks (KHP, HZ), pp. 2458–2463.
ICPRICPR-2016-Pham0PV #performance
Faster training of very deep networks via p-norm gates (TP, TT0, DQP, SV), pp. 3542–3547.
ICPRICPR-2016-PuZZ #multi
Structure and appearance preserving network flow for multi-object tracking (SP, HZ, KZ), pp. 1804–1808.
ICPRICPR-2016-RoyDB #classification #documentation #image
Generalized stacking of layerwise-trained Deep Convolutional Neural Networks for document image classification (SR, AD, UB), pp. 1273–1278.
ICPRICPR-2016-RoyTL #learning
Context-regularized learning of fully convolutional networks for scene labeling (AR, ST, LJL), pp. 3751–3756.
ICPRICPR-2016-ShankarDG #learning
Reinforcement Learning via Recurrent Convolutional Neural Networks (TS, SKD, PG), pp. 2592–2597.
ICPRICPR-2016-StunerCP #recognition #word
Cascading BLSTM networks for handwritten word recognition (BS, CC0, TP), pp. 3416–3421.
ICPRICPR-2016-SuJWSW #novel #recognition #segmentation
Novel character segmentation method for overlapped Chinese handwriting recognition based on LSTM neural networks (TS, SJ, QW, LS, RW), pp. 1141–1146.
ICPRICPR-2016-SunHLK #learning #multi #recognition
Multiple Instance Learning Convolutional Neural Networks for object recognition (MS, TXH, MCL, AKR), pp. 3270–3275.
ICPRICPR-2016-TabernikKWL #composition #towards
Towards deep compositional networks (DT, MK, JLW, AL), pp. 3470–3475.
ICPRICPR-2016-Teerapittayanon #named #performance
BranchyNet: Fast inference via early exiting from deep neural networks (ST, BM, HTK), pp. 2464–2469.
ICPRICPR-2016-TobiasDRMF #case study #mobile #recognition
Convolutional Neural Networks for object recognition on mobile devices: A case study (LT, AD, FR0, GM, RF), pp. 3530–3535.
ICPRICPR-2016-Triantafyllidou #detection #incremental #learning
Face detection based on deep convolutional neural networks exploiting incremental facial part learning (DT, AT), pp. 3560–3565.
ICPRICPR-2016-Uchida0O
Coupled convolution layer for convolutional neural network (KU, MT0, MO), pp. 3548–3553.
ICPRICPR-2016-Valverde-Rebaza #predict #social
Exploiting social and mobility patterns for friendship prediction in location-based social networks (JCVR, MR, PP, AdAL), pp. 2526–2531.
ICPRICPR-2016-VargaS #automation #image
Fully automatic image colorization based on Convolutional Neural Network (DV, TS), pp. 3691–3696.
ICPRICPR-2016-WangB #predict
Link prediction via Supervised Dynamic Network Formation (YW0, LB0), pp. 4160–4165.
ICPRICPR-2016-WangLLGTO #gesture #recognition #scalability #using
Large-scale Isolated Gesture Recognition using Convolutional Neural Networks (PW, WL, SL, ZG, CT, PO), pp. 7–12.
ICPRICPR-2016-WangLLZGO #gesture #recognition #scalability #using
Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks (PW, WL, SL, YZ, ZG, PO), pp. 13–18.
ICPRICPR-2016-WangLP #visual notation
Finetuning Convolutional Neural Networks for visual aesthetics (YW, YL, FP), pp. 3554–3559.
ICPRICPR-2016-WangWH #analysis #using
Network entropy analysis using the Maxwell-Boltzmann partition function (JW, RCW0, ERH), pp. 1321–1326.
ICPRICPR-2016-Williams #classification #using
Underwater target classification in synthetic aperture sonar imagery using deep convolutional neural networks (DPW), pp. 2497–2502.
ICPRICPR-2016-XieSJFZ #recognition
Fully convolutional recurrent network for handwritten Chinese text recognition (ZX, ZS, LJ, ZF, SZ), pp. 4011–4016.
ICPRICPR-2016-XuT #3d #learning
Beam search for learning a deep Convolutional Neural Network of 3D shapes (XX, ST), pp. 3506–3511.
ICPRICPR-2016-Yamada #detection
Pedestrian detection with a resolution-aware convolutional network (KY), pp. 591–596.
ICPRICPR-2016-YamashitaFYF #detection #multi #using
Pedestrian and part position detection using a regression-based multiple task deep convolutional neural network (TY, HF, YY, HF), pp. 3500–3505.
ICPRICPR-2016-YangJNL #normalisation #online #recognition #using
Rotation-free online handwritten character recognition using dyadic path signature features, hanging normalization, and deep neural network (WY, LJ, HN, TL), pp. 4083–4088.
ICPRICPR-2016-YangN #detection #multi
A multi-scale cascade fully convolutional network face detector (ZY, RN), pp. 633–638.
ICPRICPR-2016-YangSZ #detection
A joint facial point detection method of deep convolutional network and shape regression (TY, CS, NZ), pp. 543–548.
ICPRICPR-2016-Ye0L #3d #retrieval #sketching
3D sketch-based 3D model retrieval with convolutional neural network (YY, BL0, YL), pp. 2936–2941.
ICPRICPR-2016-YeZL #classification #documentation #online #random
Joint training of conditional random fields and neural networks for stroke classification in online handwritten documents (JYY, YMZ, CLL), pp. 3264–3269.
ICPRICPR-2016-ZhangGST #recognition
Application of pronunciation knowledge on phoneme recognition by LSTM neural network (BZ, YG, YS, BT), pp. 2906–2911.
ICPRICPR-2016-ZhangLQ #using
Wake-up-word spotting using end-to-end deep neural network system (SZ, WL, YQ0), pp. 2878–2883.
ICPRICPR-2016-ZhangQ #adaptation #agile #modelling
Rapid feature space MLLR speaker adaptation for deep neural network acoustic modeling (SZ, YQ0), pp. 2889–2894.
ICPRICPR-2016-ZhengCZZL #independence #using
Text-independent voice conversion using deep neural network based phonetic level features (HZ, WC, TZ, SZ, ML0), pp. 2872–2877.
ICPRICPR-2016-ZhongZYL #recognition
Handwritten Chinese character recognition with spatial transformer and deep residual networks (ZZ, XYZ, FY, CLL), pp. 3440–3445.
ICPRICPR-2016-ZhuWLZ #gender #learning #lightweight #recognition
Learning a lightweight deep convolutional network for joint age and gender recognition (LZ, KW, LL, LZ0), pp. 3282–3287.
ICPRICPR-2016-ZhuZMSSS #3d #gesture #recognition #scalability #using
Large-scale Isolated Gesture Recognition using pyramidal 3D convolutional networks (GZ, LZ0, LM, JS, JS, PS), pp. 19–24.
KDDKDD-2016-ArbourGJ
Inferring Network Effects from Observational Data (DTA, DG, DDJ), pp. 715–724.
KDDKDD-2016-BaoWL #approach #data-driven #predict #resource management
From Prediction to Action: A Closed-Loop Approach for Data-Guided Network Resource Allocation (YB, HW, XL0), pp. 1425–1434.
KDDKDD-2016-ChangZTYCHH #learning #streaming
Positive-Unlabeled Learning in Streaming Networks (SC, YZ0, JT, DY, YC, MAHJ, TSH), pp. 755–764.
KDDKDD-2016-Chayes #estimation #machine learning #modelling
Graphons and Machine Learning: Modeling and Estimation of Sparse Massive Networks (JTC), p. 1.
KDDKDD-2016-ChenTXYH #dependence #multi #named #performance
FASCINATE: Fast Cross-Layer Dependency Inference on Multi-layered Networks (CC0, HT, LX, LY, QH), pp. 765–774.
KDDKDD-2016-ChenWTWC
Compressing Convolutional Neural Networks in the Frequency Domain (WC, JTW, ST, KQW, YC), pp. 1475–1484.
KDDKDD-2016-ChuWPWZC
Finding Gangs in War from Signed Networks (LC, ZW, JP, JW, ZZ, EC), pp. 1505–1514.
KDDKDD-2016-CoskunGK #performance #proximity #query
Efficient Processing of Network Proximity Queries via Chebyshev Acceleration (MC, AG, MK), pp. 1515–1524.
KDDKDD-2016-DengSDZYL #predict
Latent Space Model for Road Networks to Predict Time-Varying Traffic (DD, CS, UD, LZ, RY, YL0), pp. 1525–1534.
KDDKDD-2016-Freitas #composition #learning
Learning to Learn and Compositionality with Deep Recurrent Neural Networks: Learning to Learn and Compositionality (NdF), p. 3.
KDDKDD-2016-GroverL #learning #named #scalability
node2vec: Scalable Feature Learning for Networks (AG, JL), pp. 855–864.
KDDKDD-2016-GuoLI #approximate
Convolutional Neural Networks for Steady Flow Approximation (XG, WL, FI), pp. 481–490.
KDDKDD-2016-HaPK #categorisation #e-commerce #multi #scalability #using
Large-Scale Item Categorization in e-Commerce Using Multiple Recurrent Neural Networks (JH, HP, JK), pp. 107–115.
KDDKDD-2016-HuangZCSML #scalability
Meta Structure: Computing Relevance in Large Heterogeneous Information Networks (ZH0, YZ, RC, YS, NM, XL), pp. 1595–1604.
KDDKDD-2016-Li0TFT #named
QUINT: On Query-Specific Optimal Networks (LL, YY0, JT0, WF0, HT), pp. 985–994.
KDDKDD-2016-LiuPLTCL #online #social
Audience Expansion for Online Social Network Advertising (HL, DP, KL, MT, FC, CL), pp. 165–174.
KDDKDD-2016-NandanwarM #classification
Structural Neighborhood Based Classification of Nodes in a Network (SN, MNM), pp. 1085–1094.
KDDKDD-2016-PerozziSST #recommendation
When Recommendation Goes Wrong: Anomalous Link Discovery in Recommendation Networks (BP, MS, JS, MT), pp. 569–578.
KDDKDD-2016-Robles-GrandaMN #generative #modelling
Sampling of Attributed Networks from Hierarchical Generative Models (PRG, SM, JN), pp. 1155–1164.
KDDKDD-2016-Shi0CTGR #multi #scalability #social
Dynamics of Large Multi-View Social Networks: Synergy, Cannibalization and Cross-View Interplay (YS, MK0, SC, MT, SG, RR), pp. 1855–1864.
KDDKDD-2016-WangC0
Structural Deep Network Embedding (DW, PC0, WZ0), pp. 1225–1234.
KDDKDD-2016-XuYYXZ #detection
Talent Circle Detection in Job Transition Networks (HX, ZY0, JY, HX, HZ), pp. 655–664.
KDDKDD-2016-XuZZLZCX #analysis #behaviour #perspective #social
Taxi Driving Behavior Analysis in Latent Vehicle-to-Vehicle Networks: A Social Influence Perspective (TX, HZ, XZ, QL0, HZ, EC, HX), pp. 1285–1294.
KDDKDD-2016-YuCG #identification #social
Identifying Decision Makers from Professional Social Networks (SY, EC, AG), pp. 333–342.
KDDKDD-2016-ZangCF #social
Beyond Sigmoids: The NetTide Model for Social Network Growth, and Its Applications (CZ, PC0, CF), pp. 2015–2024.
KDDKDD-2016-ZhaiCZZ #learning #named #online
DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks (SZ, KhC, RZ, Z(Z), pp. 1295–1304.
KDDKDD-2016-ZhangT #named #performance
FINAL: Fast Attributed Network Alignment (SZ, HT), pp. 1345–1354.
KDDKDD-2016-ZhangXKZ #evolution #named
NetCycle: Collective Evolution Inference in Heterogeneous Information Networks (YZ, YX, XK, YZ), pp. 1365–1374.
PLDIPLDI-2016-El-HassanyMBVV #analysis #concurrent #named
SDNRacer: concurrency analysis for software-defined networks (AEH, JM, PB, LV, MTV), pp. 402–415.
PLDIPLDI-2016-McClurgHFC #programming
Event-driven network programming (JM, HH, NF, PC), pp. 369–385.
PLDIPLDI-2016-TruongBTLMFS #compilation #named #performance #runtime
Latte: a language, compiler, and runtime for elegant and efficient deep neural networks (LT, RB, ET, HL, CM, AF, TS), pp. 209–223.
POPLPOPL-2016-PlotkinBLRV #scalability #symmetry #using #verification
Scaling network verification using symmetry and surgery (GDP, NB, NPL, AR, GV), pp. 69–83.
ASEASE-2016-AbdessalemNBS #multi #testing #using
Testing advanced driver assistance systems using multi-objective search and neural networks (RBA, SN, LCB, TS), pp. 63–74.
ASEASE-2016-XuYXXCL #developer #online #predict #semantics
Predicting semantically linkable knowledge in developer online forums via convolutional neural network (BX, DY, ZX, XX, GC, SL), pp. 51–62.
FSEFSE-2016-Alexandru #synthesis #using
Guided code synthesis using deep neural networks (CVA), pp. 1068–1070.
CASECASE-2016-GaddouriBD #hybrid #modelling #petri net
Controlled Triangular Batches Petri Nets for hybrid mesoscopic modeling of traffic road networks under VSL control (RG, LB, ID), pp. 427–432.
CASECASE-2016-LiuT #distributed #hybrid #multi #using
Distributed vision network for multiple target tracking using a dynamic hybrid consensus filter (GL, GT), pp. 805–808.
CASECASE-2016-ZouFP #estimation #metric #using
Psychoacoustic impacts estimation in manufacturing based on accelerometer measurement using artificial neural networks (MZ, LF, JP), pp. 1203–1208.
ESOPESOP-2016-BresGH #algebra #process
A Timed Process Algebra for Wireless Networks with an Application in Routing - (Extended Abstract) (EB, RJvG, PH), pp. 95–122.
ICTSSICTSS-2016-SuzukiPKT #analysis #behaviour #visualisation
Distribution Visualization for User Behavior Analysis on LTE Network (MS, QP, TK, MT), pp. 249–255.
CBSECBSE-2015-RamachandranDPM
Hitch Hiker: A Remote Binding Model with Priority Based Data Aggregation for Wireless Sensor Networks (GSR, WD, JP, SM, WJ, DH, BP), pp. 43–48.
ECSAECSA-2015-WangC #architecture #performance #social
A Specialised Social Network Software Architecture for Efficient Household Water Use Management (ZW, AC), pp. 146–153.
DRRDRR-2015-MiouletBCPB #architecture #multi #recognition
Exploring multiple feature combination strategies with a recurrent neural network architecture for off-line handwriting recognition (LM, GB, CC, TP, SB).
HTHT-2015-AhlersM #challenge #collaboration #concept #semantics
Everything is Filed under “File”: Conceptual Challenges in Applying Semantic Search to Network Shares for Collaborative Work (DA, MM), pp. 327–328.
HTHT-2015-HeimbachSSH #empirical #facebook #online #social #twitter
Content Virality on Online Social Networks: Empirical Evidence from Twitter, Facebook, and Google+ on German News Websites (IH, BS, TS, OH), pp. 39–47.
HTHT-2015-JainKJ #online #social
Other Times, Other Values: Leveraging Attribute History to Link User Profiles across Online Social Networks (PJ, PK, AJ), pp. 247–255.
HTHT-2015-KershawRS #online #social
Language Innovation and Change in On-line Social Networks (DK, MR, PS), pp. 311–314.
SIGMODSIGMOD-2015-ErlingALCGPPB #benchmark #interactive #metric #social
The LDBC Social Network Benchmark: Interactive Workload (OE, AA, JLLP, HC, AG, APP, MDP, PAB), pp. 619–630.
SIGMODSIGMOD-2015-GurukarRR #approach #commit #communication #mining #named #scalability
COMMIT: A Scalable Approach to Mining Communication Motifs from Dynamic Networks (SG, SR, BR), pp. 475–489.
SIGMODSIGMOD-2015-JiangFW #keyword #scalability
Exact Top-k Nearest Keyword Search in Large Networks (MJ, AWCF, RCWW), pp. 393–404.
SIGMODSIGMOD-2015-LiBCGM #named #towards
GetReal: Towards Realistic Selection of Influence Maximization Strategies in Competitive Networks (HL, SSB, JC, YG, JM), pp. 1525–1537.
SIGMODSIGMOD-2015-MytilinisGKDTTG #distributed #framework #named #platform #social
MoDisSENSE: A Distributed Spatio-Temporal and Textual Processing Platform for Social Networking Services (IM, IG, IK, KD, DT, MT, LG, NK), pp. 895–900.
SIGMODSIGMOD-2015-WangLYXZ #approach #performance
Efficient Route Planning on Public Transportation Networks: A Labelling Approach (SW, WL, YY, XX, SZ), pp. 967–982.
VLDBVLDB-2015-LiQYM #community #scalability
Influential Community Search in Large Networks (RHL, LQ, JXY, RM), pp. 509–520.
VLDBVLDB-2015-NaziZT0D #online #performance #social
Walk, Not Wait: Faster Sampling Over Online Social Networks (AN, ZZ, ST, NZ, GD), pp. 678–689.
VLDBVLDB-2015-WangWYZ #benchmark #community #detection #framework #metric #social
Community Detection in Social Networks: An In-depth Benchmarking Study with a Procedure-Oriented Framework (MW, CW, JXY, JZ), pp. 998–1009.
VLDBVLDB-2015-YuM #performance #scalability
Efficient Partial-Pairs SimRank Search for Large Networks (WY, JAM), pp. 569–580.
VLDBVLDB-2015-Zhou0D #online #performance #social
Leveraging History for Faster Sampling of Online Social Networks (ZZ, NZ, GD), pp. 1034–1045.
EDMEDM-2015-AnayaGLH #analysis #approach #collaboration #diagrams #social #using
An Approach of Collaboration Analytics in MOOCs Using Social Network Analysis and Influence Diagram (ARA, JGB, EL, FHdO), pp. 492–495.
EDMEDM-2015-Eagle #interactive
Estimating the Local Size and Coverage of Interaction Network Regions (ME), pp. 671–673.
EDMEDM-2015-EagleHB #estimation #interactive #predict #problem
Interaction Network Estimation: Predicting Problem-Solving Diversity in Interactive Environments (ME, AH, TB), pp. 342–349.
EDMEDM-2015-Hayashi #analysis #feedback #online #process
Psychological Effects of In-Group Activity Feedback in an Online Explanation Task: Lexical Network Analysis (YH), pp. 484–487.
EDMEDM-2015-JiangZLL #analysis #question #what
Influence Analysis by Heterogeneous Network in MOOC Forums: What can We Discover? (ZJ, YZ, CL, XL), pp. 242–249.
SIGITESIGITE-2015-ChouH #research
Theoretical Research Project vs. Hands-on Project in a Network Management Course (TSC, EH), pp. 145–148.
SIGITESIGITE-2015-FeasterZH
Serious Toys: Introducing Sensors and Sensor Networks in Pre-collegiate Classrooms (YF, JZ, JOH), pp. 3–8.
SIGITESIGITE-2015-StrongGS #performance
Work in Progress: Improving the Performance of the Radial Basis Function Network (AS, TG, LS), p. 103.
ICALPICALP-v2-2015-AminofRZS #liveness
Liveness of Parameterized Timed Networks (BA, SR, FZ, FS), pp. 375–387.
ICALPICALP-v2-2015-AvinLNP
Core Size and Densification in Preferential Attachment Networks (CA, ZL, YN, DP), pp. 492–503.
ICALPICALP-v2-2015-BringmannFHRS
Ultra-Fast Load Balancing on Scale-Free Networks (KB, TF, MH, RR, TS), pp. 516–527.
ICALPICALP-v2-2015-Charron-BostFN #algorithm #approximate
Approximate Consensus in Highly Dynamic Networks: The Role of Averaging Algorithms (BCB, MF, TN), pp. 528–539.
ICALPICALP-v2-2015-KarbasiLS #normalisation
Normalization Phenomena in Asynchronous Networks (AK, JL, AS), pp. 688–700.
LATALATA-2015-CodishCS #game studies #sorting
Sorting Networks: The End Game (MC, LCF, PSK), pp. 664–675.
LATALATA-2015-LiH #algebra #automaton #on the
On Observability of Automata Networks via Computational Algebra (RL, YH), pp. 249–262.
FMFM-2015-NelsonFK #difference #program analysis
Static Differential Program Analysis for Software-Defined Networks (TN, ADF, SK), pp. 395–413.
RTARTA-2015-Hellstrom #algebra
Network Rewriting II: Bi- and Hopf Algebras (LH), pp. 194–208.
CHI-PLAYCHI-PLAY-2015-EagleRHBBAE #interactive #learning
Measuring Implicit Science Learning with Networks of Player-Game Interactions (ME, ER, DH, RB, TB, JAC, TE), pp. 499–504.
CHI-PLAYCHI-PLAY-2015-Samper-Martinez #game studies #quote #social
“After All the Time I Put Into This”: Co-Creation and the End-of-life of Social Network Games (ASM, KMG, EGA, BK, SWL), pp. 135–140.
CHI-PLAYCHI-PLAY-2015-TondelloWSLKN #game studies
CHI PLAYGUE: A Networking Game of Emergent Sociality (GFT, RRW, SNS, AL, RK, LEN), pp. 791–794.
CoGCIG-2015-Miikkulainen #evolution #tutorial
Tutorial III: Evolving neural networks (RM), p. 22.
CoGCIG-2015-ZhuangLPZ #monte carlo #novel #representation
Improving Monte-Carlo tree search for dots-and-boxes with a novel board representation and artificial neural networks (YZ, SL, TVP, CZ), pp. 314–321.
DiGRADiGRA-2015-PaavilainenKKA #case study #experience #game studies #social
Exploring Playful Experiences in Social Network Games (JP, EK, HK, KA).
FDGFDG-2015-Vrajitoru #game studies
A Pattern-Based Bayesian Network for the Game of Nine Men's Morris (DV).
GaMGaM-2015-ValletKPM #approach #modelling #social #visual notation
A Visual Analytics Approach to Compare Propagation Models in Social Networks (JV, HK, BP, GM), pp. 65–79.
CSCWCSCW-2015-Borge-Holthoefer #twitter
Content and Network Dynamics Behind Egyptian Political Polarization on Twitter (JBH, WM, KD, IW), pp. 700–711.
CSCWCSCW-2015-Ferro #social
The Importance of Publicly Available Social Networking Sites (SNSs) to Entrepreneurs (TF), pp. 917–928.
CSCWCSCW-2015-GuhaW #social
Do Birds of a Feather Watch Each Other?: Homophily and Social Surveillance in Location Based Social Networks (SG, SBW), pp. 1010–1020.
CSCWCSCW-2015-IkkalaL
Monetizing Network Hospitality: Hospitality and Sociability in the Context of Airbnb (TI, AL), pp. 1033–1044.
CSCWCSCW-2015-IslamP #social
Engagement and Well-being on Social Network Sites (AKMNI, SP), pp. 375–382.
CSCWCSCW-2015-Medhi-ThiesFGOC #named #social
KrishiPustak: A Social Networking System for Low-Literate Farmers (IMT, PF, NG, JO, EC), pp. 1670–1681.
CSCWCSCW-2015-ParkKLYJC #case study #facebook #social
Manifestation of Depression and Loneliness on Social Networks: A Case Study of Young Adults on Facebook (SP, IK, SWL, JY, BJ, MC), pp. 557–570.
CSCWCSCW-2015-SleeperACKMS #behaviour #social
I Would Like To..., I Shouldn’t..., I Wish I...: Exploring Behavior-Change Goals for Social Networking Sites (MS, AA, LFC, PGK, SAM, NMS), pp. 1058–1069.
CSCWCSCW-2015-WisniewskiIKP #privacy #social
Give Social Network Users the Privacy They Want (PJW, AKMNI, BPK, SP), pp. 1427–1441.
HCIDUXU-DD-2015-Frankjaer #smarttech #social
Soft Computation in the Public Sphere: Enhancing Social Dynamics with Wearable Networks (TRF), pp. 447–457.
HCIDUXU-IXD-2015-CandelloBC #design #process #social
Design Process of a Social Network System for Storage and Share Files in the Workplace (HC, SB, LC), pp. 13–24.
HCIDUXU-IXD-2015-GuYWD #behaviour #design #interactive #social #visualisation
Visualizing Group User Behaviors for Social Network Interaction Design Iteration (ZG, JMY, ZW, ZD), pp. 36–45.
HCIHCI-IT-2015-FioriniLEMMBCD #case study #interactive #interface #social
Enhancing Human Robot Interaction Through Social Network Interfaces: A Case Study (LF, RL, RE, AM, AM, MB, FC, PD), pp. 729–740.
HCIHCI-IT-2015-MullerLBSKSW #data-driven #overview #predict #using
Using Neural Networks for Data-Driven Backchannel Prediction: A Survey on Input Features and Training Techniques (MM, DL, LB, MS, KK, SS, AW), pp. 329–340.
HCIHIMI-IKD-2015-NoseLBK #approach
Centralized Approach for a Unified Wireless Network Access (JDN, JL, CB, AK), pp. 547–559.
HCILCT-2015-Garcia-PenalvoC #evolution #information management #social
Evolution of the Conversation and Knowledge Acquisition in Social Networks Related to a MOOC Course (FJGP, JCB, OBG, ÁFB), pp. 470–481.
HCISCSM-2015-AhnL #social
An Analytic Study on Private SNS for Bonding Social Networking (HA, SL), pp. 107–117.
HCISCSM-2015-Kanawati #community #detection
Ensemble Selection for Community Detection in Complex Networks (RK), pp. 138–147.
HCISCSM-2015-KastratiIYD #analysis #online #social #using
Analysis of Online Social Networks Posts to Investigate Suspects Using SEMCON (ZK, ASI, SYY, FD), pp. 148–157.
HCISCSM-2015-ManssourSSFS #image #social #using #visualisation
Using Information Visualization Techniques to Improve the Perception of the Organizations’ Image on Social Networks (IHM, MSS, CQS, AJF, FTS), pp. 55–66.
HCISCSM-2015-VillelaXP #collaboration #identification
Identifying Collaboration Strategies in Scientific Collaboration Networks (MLBV, SX, ROP), pp. 253–264.
CAiSECAiSE-2015-SenderovichWGMK #process #validation
Discovery and Validation of Queueing Networks in Scheduled Processes (AS, MW, AG, AM, SK, CAB), pp. 417–433.
ICEISICEIS-v1-2015-CerqueiraOG #community #framework #scalability #social
A Framework for Analysing Dynamic Communities in Large-scale Social Networks (VC, MDBO, JG), pp. 235–242.
ICEISICEIS-v1-2015-CoelhoC
Radial Basis Function Neural Network Receiver for Wireless Channels (PHGC, FMC), pp. 658–663.
ICEISICEIS-v1-2015-SarmentoCG #streaming #using
Streaming Networks Sampling using top-K Networks (RS, MC, JG), pp. 228–234.
ICEISICEIS-v1-2015-ShamsuzzohaEAH #case study
Tracking and Tracing of Global Supply Chain Network — Case Study from a Finnish Company (AS, ME, RAT, PTH), pp. 46–53.
ICEISICEIS-v1-2015-YanguiNG #concept #design #multi #social #towards
Towards Data Warehouse Schema Design from Social Networks — Dynamic Discovery of Multidimensional Concepts (RY, AN, FG), pp. 338–345.
ICEISICEIS-v2-2015-MikhaylovFSZFT #locality #process
System of Localisation of the Network Activity Source in APCS Data Lines (DM, SDF, YYS, AZ, ASF, AT), pp. 245–251.
ICEISICEIS-v2-2015-PieroniP #configuration management #domain-specific language
A DSL for Configuration Management of Integrated Network Management System (RP, RADP), pp. 355–364.
ICEISICEIS-v2-2015-SmirnovP #architecture #hybrid #peer-to-peer #privacy #recommendation
Privacy-preserving Hybrid Peer-to-Peer Recommendation System Architecture — Locality-Sensitive Hashing in Structured Overlay Network (AVS, AP), pp. 532–542.
ICEISICEIS-v3-2015-BagheriKT #information management #social
Business-IT Alignment in PSS Value Networks — Linking Customer Knowledge Management to Social Customer Relationship Management (SB, RJK, JJMT), pp. 249–257.
ICEISICEIS-v3-2015-KypriotakiZG #distributed #peer-to-peer
From Bitcoin to Decentralized Autonomous Corporations — Extending the Application Scope of Decentralized Peer-to-Peer Networks and Blockchains (KNK, EDZ, GMG), pp. 284–290.
ICEISICEIS-v3-2015-RosaZ #mobile #privacy #social
Location-sharing Model in Mobile Social Networks with Privacy Guarantee (TAR, SDZ), pp. 44–54.
CIKMCIKM-2015-AnwarLV0 #estimation #named
RoadRank: Traffic Diffusion and Influence Estimation in Dynamic Urban Road Networks (TA, CL, HLV, MSI0), pp. 1671–1674.
CIKMCIKM-2015-DingSGHYH #predict #sentiment #video
Video Popularity Prediction by Sentiment Propagation via Implicit Network (WD, YS, LG, XH, RY, TH), pp. 1621–1630.
CIKMCIKM-2015-GuoWWT #social #topic
Social-Relational Topic Model for Social Networks (WG, SW, LW0, TT), pp. 1731–1734.
CIKMCIKM-2015-HeLHTD #social
Extracting Interest Tags for Non-famous Users in Social Network (WH, HL, JH0, ST, XD0), pp. 861–870.
CIKMCIKM-2015-HeWJ #canonical #correlation #documentation #topic
Discovering Canonical Correlations between Topical and Topological Information in Document Networks (YH0, CW0, CJ), pp. 1281–1290.
CIKMCIKM-2015-JoKB #analysis #gpu #matrix #multi #performance #scalability #social
Efficient Sparse Matrix Multiplication on GPU for Large Social Network Analysis (YYJ, SWK, DHB), pp. 1261–1270.
CIKMCIKM-2015-KangGWM #algorithm #clustering #scalability
Scalable Clustering Algorithm via a Triangle Folding Processing for Complex Networks (YK, XG, WW0, DM), pp. 33–42.
CIKMCIKM-2015-LiP #multi #named #privacy
ReverseCloak: Protecting Multi-level Location Privacy over Road Networks (CL0, BP), pp. 673–682.
CIKMCIKM-2015-MaoLF #approach #recognition #transaction
Fraud Transaction Recognition: A Money Flow Network Approach (RM, ZL, JF), pp. 1871–1874.
CIKMCIKM-2015-MiyauchiK #algorithm #community #detection #novel #quality #what
What Is a Network Community?: A Novel Quality Function and Detection Algorithms (AM0, YK), pp. 1471–1480.
CIKMCIKM-2015-RezvaniLXL #identification #scalability #social
Identifying Top-k Structural Hole Spanners in Large-Scale Social Networks (MR, WL, WX, CL), pp. 263–272.
CIKMCIKM-2015-ShiZLYYW #personalisation #recommendation #semantics
Semantic Path based Personalized Recommendation on Weighted Heterogeneous Information Networks (CS, ZZ, PL, PSY, YY, BW0), pp. 453–462.
CIKMCIKM-2015-SiersdorferKAZ #how #scalability #social #using
Who With Whom And How?: Extracting Large Social Networks Using Search Engines (SS, PK, HA, SZ), pp. 1491–1500.
CIKMCIKM-2015-SongHL #mining #social
Mining Brokers in Dynamic Social Networks (CS, WH, MLL), pp. 523–532.
CIKMCIKM-2015-TuarobTSR #modelling #social #using
Modeling Individual-Level Infection Dynamics Using Social Network Information (ST, CST, MS, NR), pp. 1501–1510.
CIKMCIKM-2015-WanLKYGCH #classification #learning
Classification with Active Learning and Meta-Paths in Heterogeneous Information Networks (CW, XL, BK, XY, QG, DWLC, JH0), pp. 443–452.
CIKMCIKM-2015-Wei0LQST #knowledge base #scalability
Large-scale Knowledge Base Completion: Inferring via Grounding Network Sampling over Selected Instances (ZW, JZ0, KL0, ZQ, ZS, GT), pp. 1331–1340.
CIKMCIKM-2015-YeL #constraints #logic #markov #multi
Structural Constraints for Multipartite Entity Resolution with Markov Logic Network (TY, HWL), pp. 1691–1694.
CIKMCIKM-2015-ZhaoZK #analysis #game studies #recommendation #social
Exploiting Game Theoretic Analysis for Link Recommendation in Social Networks (TZ, HVZ, IK), pp. 851–860.
CIKMCIKM-2015-ZhouCLXZL #social
Location-Based Influence Maximization in Social Networks (TZ, JC, BL0, SX, ZZ, JL), pp. 1211–1220.
ECIRECIR-2015-LiTWLR #automation #quality #wiki
Automatically Assessing Wikipedia Article Quality by Exploiting Article-Editor Networks (XL, JT, TW, ZL, MdR), pp. 574–580.
ECIRECIR-2015-WangHS0W0 #problem #recommendation #social #towards
Toward the New Item Problem: Context-Enhanced Event Recommendation in Event-Based Social Networks (ZW, PH, LS, KC, SW, GC), pp. 333–338.
ICMLICML-2015-AnBB #how #linear #question
How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances? (SA, FB, MB), pp. 514–523.
ICMLICML-2015-BachmanP #collaboration #generative #probability
Variational Generative Stochastic Networks with Collaborative Shaping (PB, DP), pp. 1964–1972.
ICMLICML-2015-BlundellCKW #nondeterminism
Weight Uncertainty in Neural Network (CB, JC, KK, DW), pp. 1613–1622.
ICMLICML-2015-ChenWTWC
Compressing Neural Networks with the Hashing Trick (WC, JTW, ST, KQW, YC), pp. 2285–2294.
ICMLICML-2015-ChungGCB #feedback
Gated Feedback Recurrent Neural Networks (JC, ÇG, KC, YB), pp. 2067–2075.
ICMLICML-2015-ClarkS #game studies
Training Deep Convolutional Neural Networks to Play Go (CC, AJS), pp. 1766–1774.
ICMLICML-2015-FouldsKG #framework #modelling #probability #programming #topic
Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models (JRF, SHK, LG), pp. 777–786.
ICMLICML-2015-GregorDGRW #generative #image #named
DRAW: A Recurrent Neural Network For Image Generation (KG, ID, AG, DJR, DW), pp. 1462–1471.
ICMLICML-2015-HeRFGL #modelling #named #topic
HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades (XH, TR, JRF, LG, YL), pp. 871–880.
ICMLICML-2015-Hernandez-Lobato15b #learning #probability #scalability
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks (JMHL, RA), pp. 1861–1869.
ICMLICML-2015-HongYKH #learning #online
Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network (SH, TY, SK, BH), pp. 597–606.
ICMLICML-2015-IoffeS #normalisation
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (SI, CS), pp. 448–456.
ICMLICML-2015-JozefowiczZS #architecture #empirical
An Empirical Exploration of Recurrent Network Architectures (RJ, WZ, IS), pp. 2342–2350.
ICMLICML-2015-LiSZ #generative
Generative Moment Matching Networks (YL, KS, RSZ), pp. 1718–1727.
ICMLICML-2015-LongC0J #adaptation #learning
Learning Transferable Features with Deep Adaptation Networks (ML, YC, JW, MJ), pp. 97–105.
ICMLICML-2015-MartensG #approximate #optimisation
Optimizing Neural Networks with Kronecker-factored Approximate Curvature (JM, RBG), pp. 2408–2417.
ICMLICML-2015-SnoekRSKSSPPA #optimisation #scalability #using
Scalable Bayesian Optimization Using Deep Neural Networks (JS, OR, KS, RK, NS, NS, MMAP, P, RPA), pp. 2171–2180.
ICMLICML-2015-SunWKM #geometry
Information Geometry and Minimum Description Length Networks (KS, JW, AK, SMM), pp. 49–58.
ICMLICML-2015-TangSX #learning
Learning Scale-Free Networks by Dynamic Node Specific Degree Prior (QT, SS, JX), pp. 2247–2255.
ICMLICML-2015-ZhaoMP #on the
On the Relationship between Sum-Product Networks and Bayesian Networks (HZ, MM, PP), pp. 116–124.
KDDKDD-2015-0002PGM #on the
On the Formation of Circles in Co-authorship Networks (TC, SP, PG, AM), pp. 109–118.
KDDKDD-2015-AgrawalGP #question #social #web
Whither Social Networks for Web Search? (RA, BG, EEP), pp. 1661–1670.
KDDKDD-2015-ChangHTQAH #architecture
Heterogeneous Network Embedding via Deep Architectures (SC, WH, JT, GJQ, CCA, TSH), pp. 119–128.
KDDKDD-2015-ChierichettiEKL #algorithm #performance #social
Efficient Algorithms for Public-Private Social Networks (FC, AE, RK, SL, VSM), pp. 139–148.
KDDKDD-2015-DongZTCW #named #predict
CoupledLP: Link Prediction in Coupled Networks (YD, JZ, JT, NVC, BW), pp. 199–208.
KDDKDD-2015-FakhraeiFSG #detection #evolution #multi #social
Collective Spammer Detection in Evolving Multi-Relational Social Networks (SF, JRF, MVSS, LG), pp. 1769–1778.
KDDKDD-2015-GogaLSTG #on the #online #reliability #scalability #social
On the Reliability of Profile Matching Across Large Online Social Networks (OG, PL, RS, RT, KPG), pp. 1799–1808.
KDDKDD-2015-Gomez-Rodriguez #machine learning #modelling #probability #problem #research #social
Diffusion in Social and Information Networks: Research Problems, Probabilistic Models and Machine Learning Methods (MGR, LS), pp. 2315–2316.
KDDKDD-2015-HallacLB #clustering #graph #optimisation #scalability
Network Lasso: Clustering and Optimization in Large Graphs (DH, JL, SB), pp. 387–396.
KDDKDD-2015-HanT #community #probability #social
Probabilistic Community and Role Model for Social Networks (YH, JT), pp. 407–416.
KDDKDD-2015-JiangZT #capacity #constraints #social
Reciprocity in Social Networks with Capacity Constraints (BJ, ZLZ, DT), pp. 457–466.
KDDKDD-2015-LinLC #framework #multi #social
A Learning-based Framework to Handle Multi-round Multi-party Influence Maximization on Social Networks (SCL, SDL, MSC), pp. 695–704.
KDDKDD-2015-LucierOS #distributed #scalability
Influence at Scale: Distributed Computation of Complex Contagion in Networks (BL, JO, YS), pp. 735–744.
KDDKDD-2015-McAuleyPL
Inferring Networks of Substitutable and Complementary Products (JJM, RP, JL), pp. 785–794.
KDDKDD-2015-MitzenmacherPPT #clique #detection #scalability
Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling (MM, JP, RP, CET, SCX), pp. 815–824.
KDDKDD-2015-OhsakaMK #evolution #performance #rank
Efficient PageRank Tracking in Evolving Networks (NO, TM, KiK), pp. 875–884.
KDDKDD-2015-RaptiSTT #multi
Virus Propagation in Multiple Profile Networks (AR, SS, KT, GT), pp. 975–984.
KDDKDD-2015-RayanaA #detection #metadata #overview
Collective Opinion Spam Detection: Bridging Review Networks and Metadata (SR, LA), pp. 985–994.
KDDKDD-2015-RenEWH #approach #automation #corpus #mining #recognition #type system
Automatic Entity Recognition and Typing from Massive Text Corpora: A Phrase and Network Mining Approach (XR, AEK, CW, JH), pp. 2319–2320.
KDDKDD-2015-SongMT #performance #recommendation
Efficient Latent Link Recommendation in Signed Networks (DS, DAM, DT), pp. 1105–1114.
KDDKDD-2015-SpasojevicLRB #social
When-To-Post on Social Networks (NS, ZL, AR, PB), pp. 2127–2136.
KDDKDD-2015-TangQM #named #predict #scalability
PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks (JT, MQ, QM), pp. 1165–1174.
KDDKDD-2015-WangSERZH #clustering #documentation
Incorporating World Knowledge to Document Clustering via Heterogeneous Information Networks (CW, YS, AEK, DR, MZ, JH), pp. 1215–1224.
KDDKDD-2015-XuCFSB #challenge #framework #scalability #social #testing
From Infrastructure to Culture: A/B Testing Challenges in Large Scale Social Networks (YX, NC, AF, OS, AB), pp. 2227–2236.
KDDKDD-2015-ZhangTMTJL #named #performance #scalability #similarity
Panther: Fast Top-k Similarity Search on Large Networks (JZ, JT, CM, HT, YJ, JL), pp. 1445–1454.
KDDKDD-2015-ZhangTYPY #consistency #named #social
COSNET: Connecting Heterogeneous Social Networks with Local and Global Consistency (YZ, JT, ZY, JP, PSY), pp. 1485–1494.
KDDKDD-2015-ZhuPCZZ #modelling #social
Modeling User Mobility for Location Promotion in Location-based Social Networks (WYZ, WCP, LJC, KZ, XZ), pp. 1573–1582.
RecSysRecSys-2015-ChaneyBE #personalisation #probability #recommendation #social #using
A Probabilistic Model for Using Social Networks in Personalized Item Recommendation (AJBC, DMB, TER), pp. 43–50.
RecSysRecSys-2015-MacedoMS #recommendation #social
Context-Aware Event Recommendation in Event-based Social Networks (AQdM, LBM, RLTS), pp. 123–130.
RecSysRecSys-2015-Salehi-AbariB #recommendation #social
Preference-oriented Social Networks: Group Recommendation and Inference (ASA, CB), pp. 35–42.
RecSysRecSys-2015-SousaDBM #analysis #named #recommendation
CNARe: Co-authorship Networks Analysis and Recommendations (GAdS, MAD, MAB, MMM), pp. 329–330.
SEKESEKE-2015-AssuncaoFLSV #automaton #generative #markov #modelling #named #predict #probability
SANGE — Stochastic Automata Networks Generator. A tool to efficiently predict events through structured Markovian models (JA, PF, LL, AS, JMV), pp. 581–584.
SEKESEKE-2015-GokhaleE #analysis #social
Social Analysis of the SEKE Co-Author Network (SSG, REK), pp. 237–243.
SEKESEKE-2015-NetoSZD #using
Using implications from FCA to represent a two mode network data (SMN, MAJS, LEZ, SMD), pp. 256–259.
SIGIRSIGIR-2015-HsiehLY #social
I See You: Person-of-Interest Search in Social Networks (HPH, CTL, RY), pp. 839–842.
SIGIRSIGIR-2015-SeverynM #learning #rank
Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks (AS, AM), pp. 373–382.
SIGIRSIGIR-2015-SeverynM15a #analysis #sentiment #twitter
Twitter Sentiment Analysis with Deep Convolutional Neural Networks (AS, AM), pp. 959–962.
SIGIRSIGIR-2015-SongNZAC #learning #multi #predict #social #volunteer
Multiple Social Network Learning and Its Application in Volunteerism Tendency Prediction (XS, LN, LZ, MA, TSC), pp. 213–222.
MoDELSMoDELS-2015-LuddeckeSSS #modelling #using
Modeling user intentions for in-car infotainment systems using Bayesian networks (DL, CS, JS, IS), pp. 378–385.
LOPSTRLOPSTR-2015-CodishCNS #library #sorting
Applying Sorting Networks to Synthesize Optimized Sorting Libraries (MC, LCF, MN, PSK), pp. 127–142.
LOPSTRLOPSTR-2015-MantelMPW #analysis #automaton #composition #data flow #using
Using Dynamic Pushdown Networks to Automate a Modular Information-Flow Analysis (HM, MMO, MP, AW), pp. 201–217.
PLDIPLDI-2015-McClurgHCF #performance #synthesis
Efficient synthesis of network updates (JM, HH, PC, NF), pp. 196–207.
POPLPOPL-2015-Chlipala15a #case study #composition #interface #parallel #thread #verification #web
From Network Interface to Multithreaded Web Applications: A Case Study in Modular Program Verification (AC), pp. 609–622.
PPDPPPDP-2015-ChenLJZL #automation #declarative #safety #source code #verification
Automated verification of safety properties of declarative networking programs (CC, LKL, LJ, WZ, BTL), pp. 79–90.
ESEC-FSEESEC-FSE-2015-ArcuriFG #automation #generative #testing
Generating TCP/UDP network data for automated unit test generation (AA, GF, JPG), pp. 155–165.
ICSEICSE-v1-2015-JoblinMASR #approach #community #developer #fine-grained
From Developer Networks to Verified Communities: A Fine-Grained Approach (MJ, WM, SA, JS, DR), pp. 563–573.
SACSAC-2015-ArbizaBSGT #internet #middleware #refactoring
Refactoring internet of things middleware through software-defined network (LMRA, LMB, CRPdS, LZG, LMRT), pp. 640–645.
SACSAC-2015-BoukAK #challenge #overview #research
Vehicular content centric network (VCCN): a survey and research challenges (SHB, SHA, DK), pp. 695–700.
SACSAC-2015-ChaudhuriMG #predict #using
QoS prediction for network data traffic using hierarchical modified regularized least squares rough support vector regression (AC, SM, SKG), pp. 659–661.
SACSAC-2015-ChoobdarRS
Discovering weighted motifs in gene co-expression networks (SC, PMPR, FMAS), pp. 10–17.
SACSAC-2015-DuanZHX #analysis #delivery #framework #performance #platform
Performance analysis for a service delivery platform in software defined network (QD, MZ, JH, CCX), pp. 2257–2262.
SACSAC-2015-ErnstKR #evaluation #performance
Performance evaluation of heterogeneous wireless networks considering competing objectives and viewpoints (JBE, SCK, JJPCR), pp. 680–687.
SACSAC-2015-FernandesPCRP #detection #metaheuristic #statistics
Statistical, forecasting and metaheuristic techniques for network anomaly detection (GF, EHMP, LFC, JJPCR, MLPJ), pp. 701–707.
SACSAC-2015-FontineleSSNM #physics #problem
A solution to the MCSP problem considering physical layer degradations in transparent optical networks (AF, IS, ACBS, JMN, FM), pp. 662–664.
SACSAC-2015-GkorouPE #distributed #trust
Trust-based collection of information in distributed reputation networks (DG, JAP, DHJE), pp. 2312–2319.
SACSAC-2015-GomesB #integration #mobile
Feasibility of information-centric networking integration into LTE mobile networks (AG, TB), pp. 627–633.
SACSAC-2015-GotoT #communication #detection #visual notation
Anomalous network communication detection system by visual pattern on a client computer (HG, TT), pp. 1263–1269.
SACSAC-2015-Grossl #modelling
Modeling dependable systems with continuous time Bayesian networks (MG), pp. 436–441.
SACSAC-2015-IqbalAB #design #framework #scheduling
Designing network servers within a hierarchical scheduling framework (ZI, LA, MB), pp. 653–658.
SACSAC-2015-JamhourPPSB
Interference aware channel assignment for structured wireless sensor networks (EJ, MEP, MCP, RDS, GGdOB), pp. 716–719.
SACSAC-2015-JeongYAYP #algorithm #interactive #search-based #using
Inference of disease-specific gene interaction network using a Bayesian network learned by genetic algorithm (DJ, YY, JA, YY, SP), pp. 47–53.
SACSAC-2015-KatsalisSPKT
Content placement in heterogeneous end-to-end virtual networks (KK, VS, TP, TK, LT), pp. 602–608.
SACSAC-2015-Khan #multi
Multi-criteria based vertical handover decision in heterogeneous wireless network (MK), pp. 720–721.
SACSAC-2015-LagoMM #estimation #power management
High speed network impacts and power consumption estimation for cloud data centers (DGdL, ERMM, DM), pp. 615–620.
SACSAC-2015-LevoratoDFF #algorithm #social
An ILS algorithm to evaluate structural balance in signed social networks (ML, LMdAD, YF, RMVdF), pp. 1117–1122.
SACSAC-2015-LiZL #mobile #personalisation #social
Integrating mobile sensing and social network for personalized health-care application (HL, QZ, KL), pp. 527–534.
SACSAC-2015-LopesFBM #communication #named #smarttech
SMARTFlow: a solution for autonomic management and control of communication networks for smart grids (YL, NCF, CAMB, DCMS), pp. 2212–2217.
SACSAC-2015-Lopez-CasadoPCM #named #novel
GlSch: a novel scheduler for a heterogeneous telescope network (MCLC, CJPdP, JCC, VFM, GOL, AJCT, JS, EM, JM, SK, FMS), pp. 2263–2270.
SACSAC-2015-MarquesRPSM #coordination #named
NVL: a coordination language for unmanned vehicle networks (ERBM, MR, JP, JBS, FM), pp. 331–334.
SACSAC-2015-MatlCD #effectiveness
Effective manycast messaging for Kademlia network (LM, TC, MJD), pp. 646–652.
SACSAC-2015-PerkusichMSGAP #approach #metric
A Bayesian network approach to assist on the interpretation of software metrics (MP, AM, LCeS, KCG, HOdA, AP), pp. 1498–1503.
SACSAC-2015-RajtmajerGMS #behaviour #game studies #online #social
An evolutionary game model for the spread of non-cooperative behavior in online social networks (SMR, CG, DM, ACS), pp. 1154–1159.
SACSAC-2015-TroisMBF
From software defined network to network defined for software (CT, MM, LCEDB, MDDF), pp. 665–668.
SACSAC-2015-Valverde-Rebaza #modelling #naive bayes #online #predict #social
A naïve Bayes model based on ovelapping groups for link prediction in online social networks (JCVR, AV, LB, TdPF, AdAL), pp. 1136–1141.
SACSAC-2015-ZappatoreLB #composition #contract #representation
SLA composition in service networks: a tool for representing relationships between SLAs and contracts (MZ, AL, MAB), pp. 1219–1224.
ASPLOSASPLOS-2015-TanQCAP #named #using
DIABLO: A Warehouse-Scale Computer Network Simulator using FPGAs (ZT, ZQ, XC, KA, DAP), pp. 207–221.
CASECASE-2015-ButtersGS #detection
Detecting and reducing redundancy in alarm networks (TDB, SG, JLS), pp. 1224–1229.
CASECASE-2015-ChengTLCH #algorithm #modelling #optimisation
Modeling and optimizing tensile strength and yield point on steel bar by artificial neural network with evolutionary algorithm (CKC, JTT, TTL, JHC, KSH), pp. 1562–1563.
CASECASE-2015-DepariFLS #industrial
Inexpensive SDR-based longwave radio controlled clock for time dissemination in industrial wireless sensor networks (AD, AF, ML, ES), pp. 125–130.
CASECASE-2015-DobslawGZ #challenge #industrial #using
Challenges for the use of data aggregation in industrial Wireless Sensor Networks (FD, MG, TZ), pp. 138–144.
CASECASE-2015-JiYA #automation #mobile #re-engineering
Automatic calibration and trajectory reconstruction of mobile robot in camera sensor network (YJ, AY, HA), pp. 206–211.
CASECASE-2015-KanCLY #automation #health #internet #mobile #towards
Mobile sensing and network analytics for realizing smart automated systems towards health Internet of Things (CK, YC, FL, HY), pp. 1072–1077.
CASECASE-2015-KanY #image #modelling #monitoring
Network models for monitoring high-dimensional image profiles (CK, HY), pp. 1078–1083.
CASECASE-2015-SrinivasanBSSR #automation #machine learning #modelling #using
Modelling time-varying delays in networked automation systems with heterogeneous networks using machine learning techniques (SS, FB, GS, BS, SR), pp. 362–368.
CASECASE-2015-UrgilesAC #design #development #web
Lighting control actuator design and development for a ZigBee network with a Web server mounted on Raspberry Pi (MVU, PEA, DPCT), pp. 714–719.
CASECASE-2015-YuAGB #industrial #metric
Realization and measurements of industrial wireless sensor and actuator networks (KY, , MG, MB), pp. 131–137.
DACDAC-2015-CavigelliMB #embedded #realtime
Accelerating real-time embedded scene labeling with convolutional networks (LC, MM, LB), p. 6.
DACDAC-2015-CongGHRY #architecture
On-chip interconnection network for accelerator-rich architectures (JC, MG, YH, GR, BY), p. 6.
DACDAC-2015-LiuKDK #data access #reduction
Network footprint reduction through data access and computation placement in NoC-based manycores (JL, JK, WD, MTK), p. 6.
DACDAC-2015-NishimiyaSS #evaluation #functional #interface #mockup #modelling
Evaluation of functional mock-up interface for vehicle power network modeling (KN, TS, SS), p. 6.
DACDAC-2015-ShreejithF #embedded #generative #security
Security aware network controllers for next generation automotive embedded systems (SS, SAF), p. 6.
DACDAC-2015-ZhanOGZ0 #approach #named #power management #towards
DimNoC: a dim silicon approach towards power-efficient on-chip network (JZ, JO, FG, JZ, YX), p. 6.
DATEDATE-2015-BalboniFB #configuration management #distributed #latency #multi #scalability #using
Synergistic use of multiple on-chip networks for ultra-low latency and scalable distributed routing reconfiguration (MB, JF, DB), pp. 806–811.
DATEDATE-2015-DengFDWLTINLCW #fault #hardware
Retraining-based timing error mitigation for hardware neural networks (JD, YF, ZD, YW, HL, OT, PI, DN, XL, YC, CW), pp. 593–596.
DATEDATE-2015-JiRML #hardware #implementation #logic #probability #using
A hardware implementation of a radial basis function neural network using stochastic logic (YJ, FR, CM, DJL), pp. 880–883.
DATEDATE-2015-MirhosseiniSFMS #energy
An energy-efficient virtual channel power-gating mechanism for on-chip networks (AM, MS, AF, MM, HSA), pp. 1527–1532.
DATEDATE-2015-MundhenkSLFC #authentication #lightweight
Lightweight authentication for secure automotive networks (PM, SS, ML, SAF, SC), pp. 285–288.
DATEDATE-2015-PsarrasSND #named #performance #scheduling
PhaseNoC: TDM scheduling at the virtual-channel level for efficient network traffic isolation (AP, IS, CN, GD), pp. 1090–1095.
DATEDATE-2015-TangXLLCWY #question
Spiking neural network with RRAM: can we use it for real-world application? (TT, LX, BL, RL, YC, YW, HY), pp. 860–865.
DATEDATE-2015-ThangamuthuCCL #analysis
Analysis of ethernet-switch traffic shapers for in-vehicle networking applications (ST, NC, PJLC, JJL), pp. 55–60.
DATEDATE-2015-ZhangWTYX #approximate #framework #named
ApproxANN: an approximate computing framework for artificial neural network (QZ, TW, YT, FY, QX), pp. 701–706.
HPCAHPCA-2015-ChrysosMRBV #named
SCOC: High-radix switches made of bufferless clos networks (NC, CM, MR, CB, BV), pp. 402–414.
HPCAHPCA-2015-FujiwaraKOMC
Augmenting low-latency HPC network with free-space optical links (IF, MK, TO, HM, HC), pp. 390–401.
HPCAHPCA-2015-WonKKJPS #scalability
Overcoming far-end congestion in large-scale networks (JW, GK, JK, TJ, MP, SS), pp. 415–427.
HPDCHPDC-2015-PokeH #named #replication #state machine
DARE: High-Performance State Machine Replication on RDMA Networks (MP, TH), pp. 107–118.
PDPPDP-2015-BlaskiewiczZBD #gpu #parallel
An Application of GPU Parallel Computing to Power Flow Calculation in HVDC Networks (PB, MZ, PB, PD), pp. 635–641.
PDPPDP-2015-FedorchenkoKC #analysis #database #design #security
Design of Integrated Vulnerabilities Database for Computer Networks Security Analysis (AF, IVK, AC), pp. 559–566.
PDPPDP-2015-HenrioMM #named #process
pNets: An Expressive Model for Parameterised Networks of Processes (LH, EM, MM), pp. 492–496.
PDPPDP-2015-LundKETLHF #data flow #execution #platform #process
Execution of Dataflow Process Networks on OpenCL Platforms (WL, SK, JE, LT, JL, JH, UF), pp. 618–625.
PDPPDP-2015-NgyenJDHDPT #framework #named
FIST: A Framework to Interleave Spiking Neural Networks on CGRAs (TN, SMAHJ, MD, AH, SD, JP, HT), pp. 751–758.
STOCSTOC-2015-Czumaj #permutation #random #using
Random Permutations using Switching Networks (AC), pp. 703–712.
TACASTACAS-2015-GiacobbeGGHPP #model checking
Model Checking Gene Regulatory Networks (MG, CCG, AG, TAH, TP, TP), pp. 469–483.
TACASTACAS-2015-NamjoshiT #analysis #process
Analysis of Dynamic Process Networks (KSN, RJT), pp. 164–178.
CAVCAV-2015-AbateBCK #adaptation #analysis #markov
Adaptive Aggregation of Markov Chains: Quantitative Analysis of Chemical Reaction Networks (AA, LB, MC, MZK), pp. 195–213.
CAVCAV-2015-FisherKPW #execution
Synthesising Executable Gene Regulatory Networks from Single-Cell Gene Expression Data (JF, ASK, NP, SW), pp. 544–560.
ICTSSICTSS-2015-KitaharaNSFA #capacity #evaluation
A Practical Evaluation Method of Network Traffic Load for Capacity Planning (TK, SN, MS, NF, SA), pp. 263–268.
ECSAECSA-2014-BerardinelliMP #analysis #design #performance
fUML-Driven Design and Performance Analysis of Software Agents for Wireless Sensor Network (LB, ADM, SP), pp. 324–339.
HTHT-2014-AbbasiZTL #social
Am i more similar to my followers or followees?: analyzing homophily effect in directed social networks (MAA, RZ, JT, HL), pp. 200–205.
HTHT-2014-ChelaruHNS #communication
Recognizing skill networks and their specific communication and connection practices (SC, EH, KDN, PS), pp. 13–23.
HTHT-2014-Hidalgo #big data #comprehension #development #social #visualisation
Big data visualization engines for understanding the development of countries, social networks, culture and cities (CAH), p. 3.
HTHT-2014-WenLTP #twitter
Twitter in academic conferences: usage, networking and participation over time (XW, YRL, CT, DP), pp. 285–290.
HTHT-2014-ZhangBR #analysis #empirical #social #social media
Empirical analysis of implicit brand networks on social media (KZ, SB, SR), pp. 190–199.
JCDLJCDL-2014-AkbarSFF #deduction #education #library #recommendation #social
Recommendation based on Deduced Social Networks in an educational digital library (MA, CAS, WF, EAF), pp. 29–38.
JCDLJCDL-2014-BarkerBM #library
Vector-Borne Disease Network digital library (MB, DB, NM), pp. 435–436.
JCDLJCDL-2014-CostaQW #repository #research
Research networks in data repositories (MRC, JQ, JW), pp. 403–406.
JCDLJCDL-2014-LarsonPT #named #social #towards
SNAC: The Social Networks and Archival Context project — Towards an archival authority cooperative (RRL, DP, AT), pp. 427–428.
JCDLJCDL-2014-LiuYGSG #approach #mining #recommendation
Full-text based context-rich heterogeneous network mining approach for citation recommendation (XL, YY, CG, YS, LG), pp. 361–370.
PODSPODS-2014-AmelootKNZ #declarative #fine-grained
Weaker forms of monotonicity for declarative networking: a more fine-grained answer to the calm-conjecture (TJA, BK, FN, DZ), pp. 64–75.
SIGMODSIGMOD-2014-ChenLWXML #algorithm #performance #query
Efficient algorithms for optimal location queries in road networks (ZC, YL, RCWW, JX, GM, CL), pp. 123–134.
SIGMODSIGMOD-2014-Dev #algorithm #community #detection #interactive #online #social
A user interaction based community detection algorithm for online social networks (HD), pp. 1607–1608.
SIGMODSIGMOD-2014-FengCBM #online #social
In search of influential event organizers in online social networks (KF, GC, SSB, SM), pp. 63–74.
SIGMODSIGMOD-2014-LevinK #pipes and filters #social #using
Stratified-sampling over social networks using mapreduce (RL, YK), pp. 863–874.
SIGMODSIGMOD-2014-PolychroniouSR #distributed
Track join: distributed joins with minimal network traffic (OP, RS, KAR), pp. 1483–1494.
SIGMODSIGMOD-2014-ShenHW #probability #web
A probabilistic model for linking named entities in web text with heterogeneous information networks (WS, JH, JW), pp. 1199–1210.
SIGMODSIGMOD-2014-ShiMWC #clustering
Density-based place clustering in geo-social networks (JS, NM, DW, DWC), pp. 99–110.
SIGMODSIGMOD-2014-TaoBHJWNELRS #automation #named
NewsNetExplorer: automatic construction and exploration of news information networks (FT, GB, JH, HJ, CW, BN, AEK, JL, XR, YS), pp. 1091–1094.
SIGMODSIGMOD-2014-XiongHN #approach #distributed #performance #query
A software-defined networking based approach for performance management of analytical queries on distributed data stores (PX, HH, JFN), pp. 955–966.
SIGMODSIGMOD-2014-ZhangCPSX #named
PrivBayes: private data release via bayesian networks (JZ, GC, CMP, DS, XX), pp. 1423–1434.
VLDBVLDB-2014-HuangBJW #realtime #scalability
Large Scale Real-time Ridesharing with Service Guarantee on Road Networks (YH, FB, RJ, XSW), pp. 2017–2028.
VLDBVLDB-2014-JiangFWX #distance #query
Hop Doubling Label Indexing for Point-to-Point Distance Querying on Scale-Free Networks (MJ, AWCF, RCWW, YX), pp. 1203–1214.
VLDBVLDB-2014-KongLH #named #social #social media
SPOT: Locating Social Media Users Based on Social Network Context (LK, ZL, YH), pp. 1681–1684.
VLDBVLDB-2014-KorulaL #algorithm #performance #social
An efficient reconciliation algorithm for social networks (NK, SL), pp. 377–388.
VLDBVLDB-2014-SongSZZ #framework #named #novel
PRESS: A Novel Framework of Trajectory Compression in Road Networks (RS, WS, BZ, YZ), pp. 661–672.
VLDBVLDB-2014-XiongH #distributed #named #performance #query
Pronto: A Software-Defined Networking based System for Performance Management of Analytical Queries on Distributed Data Stores (PX, HH), pp. 1661–1664.
SIGITESIGITE-2014-SchmidtHM #education #physics
A virtualized testbed with physical outlets for hands-on computer networking education (MS, FH, MM), pp. 3–8.
SIGITESIGITE-2014-Suthaharan #education #experience #named #question
FLaSKU — a classroom experience with teaching computer networking: is it useful to others in the field? (SS), pp. 15–20.
SIGITESIGITE-2014-Trabelsi #approach #comprehension #education #security #using
Enhancing the comprehension of network sniffing attack in information security education using a hands-on lab approach (ZT), pp. 39–44.
SIGITESIGITE-2014-WangBM #design #education #lessons learnt
Teaching a networking class for freshmen: course design and lessons learned (YW, TB, MM), pp. 9–14.
ICPCICPC-2014-ThungLOC #classification #design #diagrams #metric #using
Condensing class diagrams by analyzing design and network metrics using optimistic classification (FT, DL, MHO, MRVC), pp. 110–121.
ICALPICALP-v1-2014-KontogiannisZ #distance
Distance Oracles for Time-Dependent Networks (SCK, CDZ), pp. 713–725.
ICALPICALP-v1-2014-MertziosNRS #interactive #memory management
Determining Majority in Networks with Local Interactions and Very Small Local Memory (GBM, SEN, CR, PGS), pp. 871–882.
ICALPICALP-v2-2014-AschnerK #bound #constraints #modelling
Bounded-Angle Spanning Tree: Modeling Networks with Angular Constraints (RA, MJK), pp. 387–398.
ICALPICALP-v2-2014-AvinBLP #axiom #design #distributed
Distributed Computing on Core-Periphery Networks: Axiom-Based Design (CA, MB, ZL, DP), pp. 399–410.
ICALPICALP-v2-2014-ChalopinDLP #fault tolerance
Fault-Tolerant Rendezvous in Networks (JC, YD, AL, AP), pp. 411–422.
ICALPICALP-v2-2014-DamsHK #learning
Jamming-Resistant Learning in Wireless Networks (JD, MH, TK), pp. 447–458.
ICALPICALP-v2-2014-EmekSW
Computability in Anonymous Networks: Revocable vs. Irrecovable Outputs (YE, JS, RW), pp. 183–195.
LATALATA-2014-ArroyoCMP
Networks of Polarized Evolutionary Processors Are Computationally Complete (FA, SGC, VM, SP), pp. 101–112.
LATALATA-2014-BreveglieriCM #parsing
Shift-Reduce Parsers for Transition Networks (LB, SCR, AM), pp. 222–235.
LATALATA-2014-BundalaZ #sorting
Optimal Sorting Networks (DB, JZ), pp. 236–247.
LATALATA-2014-Martos-SalgadoR #petri net
Expressiveness of Dynamic Networks of Timed Petri Nets (MMS, FRV), pp. 516–527.
FMFM-2014-AntoninoSW #analysis #concurrent #csp #process #refinement
A Refinement Based Strategy for Local Deadlock Analysis of Networks of CSP Processes (PRGA, AS, JW), pp. 62–77.
SEFMSEFM-2014-PardoS #framework #policy #privacy #social
A Formal Privacy Policy Framework for Social Networks (RP, GS), pp. 378–392.
CoGCIG-2014-AsensioDC #evolution
Evolving Artificial Neural Networks applied to generate virtual characters (JMLA, JPD, PC0), pp. 1–5.
CoGCIG-2014-SzubertJ #difference #game studies #learning
Temporal difference learning of N-tuple networks for the game 2048 (MGS, WJ), pp. 1–8.
GT-VMTGT-VMT-2014-HusseinHDS #adaptation #modelling
Modelling Adaptive Networks: The Case of the Petrified Voters (MH, RH, VD, PS).
CHICHI-2014-BachPF #matrix #visualisation
Visualizing dynamic networks with matrix cubes (BB, EP, JDF), pp. 877–886.
CHICHI-2014-BurkeK #facebook #social
Growing closer on facebook: changes in tie strength through social network site use (MB, REK), pp. 4187–4196.
CHICHI-2014-DaviesLCEFKS #personalisation #pervasive #privacy
Personalisation and privacy in future pervasive display networks (ND, ML, SC, IE, AF, TK, BS), pp. 2357–2366.
CHICHI-2014-ForlinesMGB #crowdsourcing #predict #social
Crowdsourcing the future: predictions made with a social network (CF, SM, LG, RB), pp. 3655–3664.
CHICHI-2014-Hale #multi #twitter
Global connectivity and multilinguals in the Twitter network (SAH), pp. 833–842.
CHICHI-2014-LaureyssensCCMCM #approach #component #composition #game studies #named
ZWERM: a modular component network approach for an urban participation game (TL, TC, LC, PM, JC, AVM), pp. 3259–3268.
CHICHI-2014-McGookinBC #social
Studying digital graffiti as a location-based social network (DKM, SAB, GC), pp. 3269–3278.
CHICHI-2014-NorvalAH #recommendation #social #what
What’s on your mind?: investigating recommendations for inclusive social networking and older adults (CN, JLA, VLH), pp. 3923–3932.
CHICHI-2014-Smith-ClarkeMC #communication #mobile #using
Poverty on the cheap: estimating poverty maps using aggregated mobile communication networks (CSC, AJM, LC), pp. 511–520.
CHICHI-2014-WuA #online #social #visual notation
Visually impaired users on an online social network (SW, LAA), pp. 3133–3142.
CSCWCSCW-2014-BackstromK #analysis #facebook #social
Romantic partnerships and the dispersion of social ties: a network analysis of relationship status on facebook (LB, JMK), pp. 831–841.
CSCWCSCW-2014-ForteDMA #online #social #student #what
What do teens ask their online social networks?: social search practices among high school students (AF, MD, RMM, DEA), pp. 28–37.
CSCWCSCW-2014-GanglbauerFSG #case study #community #social
Think globally, act locally: a case study of a free food sharing community and social networking (EG, GF, ÖS, FG), pp. 911–921.
CSCWCSCW-2014-LingelNB #online
City, self, network: transnational migrants and online identity work (JL, MN, DB), pp. 1502–1510.
CSCWCSCW-2014-MarlowD #design #development #social
From rookie to all-star: professional development in a graphic design social networking site (JM, LD), pp. 922–933.
CSCWCSCW-2014-Morris #social #women
Social networking site use by mothers of young children (MRM), pp. 1272–1282.
CSCWCSCW-2014-Oeldorf-HirschHMTG #social
To search or to ask: the routing of information needs between traditional search engines and social networks (AOH, BH, MRM, JT, DG), pp. 16–27.
CSCWCSCW-2014-ZhangCG #evolution #social
Creepy but inevitable?: the evolution of social networking (HZ, MDC, JG), pp. 368–378.
HCIDHM-2014-ZhangGBD #industrial
Application of Bayesian Networks in Consumer Service Industry and Healthcare (LZ, YG, BB, VGD), pp. 484–495.
HCIDUXU-DI-2014-ArfaaW14a #social #usability
A Usability Study on Elder Adults Utilizing Social Networking Sites (JA, Y(W), pp. 50–61.
HCIDUXU-DI-2014-FrankjaerG #hybrid #smarttech
Wearable Networks, Creating Hybrid Spaces with Soft Circuits (TRF, DG), pp. 435–445.
HCIDUXU-ELAS-2014-Abdullah #monitoring #simulation
Simulation of Wireless Sensor Network for Flood Monitoring System (MA), pp. 255–264.
HCIDUXU-ELAS-2014-EmilianoSFBP
Traffic Management in Rural Networks (RE, FS, LF, JB, AP), pp. 452–461.
HCIHCI-AS-2014-Moallem
Home Networking: Smart but Complicated (AM), pp. 731–741.
HCIHIMI-AS-2014-JiangLLC #identification #sequence
Neural Networks for Identifying Civil Pilot’s Operation Sequences (ZJ, QL, YL, BC), pp. 241–252.
HCIHIMI-AS-2014-MaeshiroM #process
Polyhedron Network Model to Describe Creative Processes (TM, MM), pp. 535–545.
HCIHIMI-DE-2014-MatsunagaY #documentation
Digital Document Network System for Organizing Individual Knowledge (KM, KY), pp. 396–403.
HCIHIMI-DE-2014-PinheiroCM #social
Increasing Information Auditability for Social Network Users (AP, CC, CM), pp. 536–547.
HCIHIMI-DE-2014-ValdezSZH #clustering #platform #research #scalability #social #visualisation
Enhancing Interdisciplinary Cooperation by Social Platforms — Assessing the Usefulness of Bibliometric Social Network Visualization in Large-Scale Research Clusters (ACV, AKS, MZ, AH), pp. 298–309.
HCILCT-TRE-2014-BaldiniKLT #social #trust
European Citizens and Their Trust in Social Networks (GB, IK, JL, MT), pp. 363–374.
HCILCT-TRE-2014-OliveiraM #learning #research
Digital Identity of Researchers and Their Personal Learning Network (NRO, LM), pp. 467–477.
HCISCSM-2014-AbdallaY #online #overview #process #social #using
A Review of Using Online Social Networks for Investigative Activities (AA, SYY), pp. 3–12.
HCISCSM-2014-AhnMHNHPM #analysis #representation #semantics #social #twitter
Social Network Representation and Dissemination of Pre-Exposure Prophylaxis (PrEP): A Semantic Network Analysis of HIV Prevention Drug on Twitter (ZA, MM, JH, YN, CWH, MP, JM), pp. 160–169.
HCISCSM-2014-AlvesMA #gamification #guidelines #mobile #social
Guidelines for the Gamification in Mobile Social Networks (FPA, CM, JCA), pp. 559–570.
HCISCSM-2014-FardounAC #education #representation #social #student
Representing Students Curriculum in Social Networks (HMF, AA, APC), pp. 48–58.
HCISCSM-2014-FawziS #gender #online #social
An Investigation into Gender Role Conformity in an Online Social Networking Environment (AF, AS), pp. 322–330.
HCISCSM-2014-Fu #microblog #social
Can Network Help Chinese Microblogs Diffuse? Analyzing 118 Networks of Reposts About Social Issues in China (KwF), pp. 331–341.
HCISCSM-2014-HuangG #empirical #health #social #using
Exploring Health Care Professionals’ Attitudes of Using Social Networking Sites for Health Care: An Empirical Study (ZH, NG), pp. 365–372.
HCISCSM-2014-Kanawati #community #detection
Seed-Centric Approaches for Community Detection in Complex Networks (RK), pp. 197–208.
HCISCSM-2014-LeeLS #approach #social
A New Approach to Exploring Spatiotemporal Space in the Context of Social Network Services (JGL, KCL, DHS), pp. 221–228.
HCISCSM-2014-PensasVGKKCV #communication #social #using
Using Ambient Communication and Social Networking Technologies to Reduce Loneliness of Elders (HP, AMV, MG, TK, SK, SC, JV), pp. 91–102.
HCISCSM-2014-SchaarVZELJ #motivation #social #using
Reasons for Using Social Networks Professionally — The Influence of User Diversity on Usage Motivation (AKS, ACV, MZ, DE, AKL, EMJ), pp. 385–396.
HCISCSM-2014-ShiLYLC #development #social #validation
The Development and Validation of the Social Network Sites (SNSs) Usage Questionnaire (YS, YLLL, ZY, YL, HC), pp. 113–124.
HCISCSM-2014-TurnerH #social #what
What Does Your Profile Picture Say About You? The Accuracy of Thin-Slice Personality Judgments from Social Networking Sites Made at Zero-Acquaintance (MT, NH), pp. 506–516.
CAiSECAiSE-2014-BennacerJPQ #social
Matching User Profiles Across Social Networks (NB, CNJ, AP, GQ), pp. 424–438.
CAiSECAiSE-2014-StulpnagelOS #logic #markov #risk management
IT Risk Management with Markov Logic Networks (JvS, JO, JS), pp. 301–315.
CAiSECAiSE-2014-VergneS #community #markov #open source #using
Expert Finding Using Markov Networks in Open Source Communities (MV, AS), pp. 196–210.
ICEISICEIS-v1-2014-CoelhoAABB #using
Router Nodes Positioning for Wireless Networks Using Artificial Immune Systems (PHGC, JLMdA, JFMdA, LFdAB, AVdB), pp. 415–421.
ICEISICEIS-v1-2014-SilvaSMMS #branch #design #heuristic #problem
A Heuristic Procedure with Local Branching for the Fixed Charge Network Design Problem with User-optimal Flow (PHGS, LGS, CAdJM, PYPM, ES), pp. 384–394.
ICEISICEIS-v1-2014-TimoteoVF #analysis #case study #dataset #project management
Evaluating Artificial Neural Networks and Traditional Approaches for Risk Analysis in Software Project Management — A Case Study with PERIL Dataset (CT, MV, SF), pp. 472–479.
ICEISICEIS-v2-2014-AlanneKN #development #enterprise
Networks of Pain in ERP Development (AA, TK, EN), pp. 257–266.
ICEISICEIS-v2-2014-KaramtiTG #image #process #retrieval #using
Vectorization of Content-based Image Retrieval Process Using Neural Network (HK, MT, FG), pp. 435–439.
ICEISICEIS-v2-2014-LiuDT #reliability
Auditing Data Reliability in International Logistics — An Application of Bayesian Networks (LL, HAMD, RT), pp. 707–712.
ICEISICEIS-v2-2014-VieiraJF #analysis
A Risk Analysis Method for Selecting Service Providers in P2P Service Overlay Networks (RGV, OCAJ, AF), pp. 477–488.
CIKMCIKM-2014-DahimeneCM #named #recommendation #social
RecLand: A Recommender System for Social Networks (RD, CC, CdM), pp. 2063–2065.
CIKMCIKM-2014-FangZTSFSDZL #behaviour #game studies #modelling #social
Modeling Paying Behavior in Game Social Networks (ZF, XZ, JT, WS, ACMF, LS, YD, LZ, JL), pp. 411–420.
CIKMCIKM-2014-GuiSHB #modelling #multi #topic
Modeling Topic Diffusion in Multi-Relational Bibliographic Information Networks (HG, YS, JH, GB), pp. 649–658.
CIKMCIKM-2014-JiaDGZ #analysis #community
Analysis on Community Variational Trend in Dynamic Networks (XJ, ND, JG, AZ), pp. 151–160.
CIKMCIKM-2014-LiuXCXTY #approach #bound #linear #scalability #social
Influence Maximization over Large-Scale Social Networks: A Bounded Linear Approach (QL, BX, EC, HX, FT, JXY), pp. 171–180.
CIKMCIKM-2014-LiuXD #mining #predict
Relationship Emergence Prediction in Heterogeneous Networks through Dynamic Frequent Subgraph Mining (YL, SX, LD), pp. 1649–1658.
CIKMCIKM-2014-LuciaF #classification #knowledge-based #named
EgoCentric: Ego Networks for Knowledge-based Short Text Classification (WL, EF), pp. 1079–1088.
CIKMCIKM-2014-LuSY #identification #social
Identifying Your Customers in Social Networks (CTL, HHS, PSY), pp. 391–400.
CIKMCIKM-2014-MahdabiC #mining #recommendation #retrieval
Query-Driven Mining of Citation Networks for Patent Citation Retrieval and Recommendation (PM, FC), pp. 1659–1668.
CIKMCIKM-2014-PfeifferNB #learning #probability #using
Active Exploration in Networks: Using Probabilistic Relationships for Learning and Inference (JJPI, JN, PNB), pp. 639–648.
CIKMCIKM-2014-PhanDXPK #analysis #health #physics #process #social
Analysis of Physical Activity Propagation in a Health Social Network (NP, DD, XX, BP, DK), pp. 1329–1338.
CIKMCIKM-2014-RahmanH #strict #using
Sampling Triples from Restricted Networks using MCMC Strategy (MR, MAH), pp. 1519–1528.
CIKMCIKM-2014-ShaoKTG #case study #complexity #distance #navigation #query
Travel distance versus navigation complexity: a study on different spatial queries on road networks (JS, LK, ET, LG), pp. 1791–1794.
CIKMCIKM-2014-ShenJ #information management #multi #online #social
Controllable Information Sharing for User Accounts Linkage across Multiple Online Social Networks (YS, HJ), pp. 381–390.
CIKMCIKM-2014-ShihKRCGSP #component #detection
Component Detection in Directed Networks (YKS, SK, YR, JC, AG, TS, SP), pp. 1729–1738.
CIKMCIKM-2014-ShiWLYW #clustering
Ranking-based Clustering on General Heterogeneous Information Networks by Network Projection (CS, RW, YL, PSY, BW), pp. 699–708.
CIKMCIKM-2014-SpirinHDKB #analysis #facebook #graph #online #people #query #scalability #social
People Search within an Online Social Network: Large Scale Analysis of Facebook Graph Search Query Logs (NVS, JH, MD, KGK, MB), pp. 1009–1018.
CIKMCIKM-2014-WangWC #community #detection #named #social
CoDEM: An Ingenious Tool of Insight into Community Detection in Social Networks (MW, CW, JC), pp. 2006–2008.
CIKMCIKM-2014-YuX #interactive #learning #predict #scalability #social
Learning Interactions for Social Prediction in Large-scale Networks (XY, JX), pp. 161–170.
ECIRECIR-2014-LiakosPS #locality #on the #social
On the Effect of Locality in Compressing Social Networks (PL, KP, MS), pp. 650–655.
ECIRECIR-2014-LuoGWL #algorithm #classification #named #novel
HetPathMine: A Novel Transductive Classification Algorithm on Heterogeneous Information Networks (CL, RG, ZW, CL), pp. 210–221.
ECIRECIR-2014-ZhangZWS #recommendation
Content + Attributes: A Latent Factor Model for Recommending Scientific Papers in Heterogeneous Academic Networks (CZ, XZ, KW, JS), pp. 39–50.
ICMLICML-c1-2014-PinheiroC
Recurrent Convolutional Neural Networks for Scene Labeling (PHOP, RC), pp. 82–90.
ICMLICML-c1-2014-RooshenasL #interactive #learning
Learning Sum-Product Networks with Direct and Indirect Variable Interactions (AR, DL), pp. 710–718.
ICMLICML-c1-2014-ZhouT #generative #predict #probability
Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction (JZ, OGT), pp. 745–753.
ICMLICML-c2-2014-AndoniPV0 #learning
Learning Polynomials with Neural Networks (AA, RP, GV, LZ), pp. 1908–1916.
ICMLICML-c2-2014-AziziAG #composition #learning
Learning Modular Structures from Network Data and Node Variables (EA, EA, JEG), pp. 1440–1448.
ICMLICML-c2-2014-BengioLAY #generative #probability
Deep Generative Stochastic Networks Trainable by Backprop (YB, EL, GA, JY), pp. 226–234.
ICMLICML-c2-2014-CarlssonMRS #clustering #symmetry
Hierarchical Quasi-Clustering Methods for Asymmetric Networks (GEC, FM, AR, SS), pp. 352–360.
ICMLICML-c2-2014-CelikLL #estimation #performance #reduction
Efficient Dimensionality Reduction for High-Dimensional Network Estimation (SC, BAL, SIL), pp. 1953–1961.
ICMLICML-c2-2014-ChakrabartiFCM #multi #scalability
Joint Inference of Multiple Label Types in Large Networks (DC, SF, JC, SAM), pp. 874–882.
ICMLICML-c2-2014-DaneshmandGSS #algorithm #complexity
Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm (HD, MGR, LS, BS), pp. 793–801.
ICMLICML-c2-2014-DuLBS #information management #learning
Influence Function Learning in Information Diffusion Networks (ND, YL, MFB, LS), pp. 2016–2024.
ICMLICML-c2-2014-GravesJ #recognition #speech #towards
Towards End-To-End Speech Recognition with Recurrent Neural Networks (AG, NJ), pp. 1764–1772.
ICMLICML-c2-2014-GregorDMBW
Deep AutoRegressive Networks (KG, ID, AM, CB, DW), pp. 1242–1250.
ICMLICML-c2-2014-LevineK #learning #optimisation #policy
Learning Complex Neural Network Policies with Trajectory Optimization (SL, VK), pp. 829–837.
ICMLICML-c2-2014-LindermanA #process
Discovering Latent Network Structure in Point Process Data (SWL, RPA), pp. 1413–1421.
ICMLICML-c2-2014-MnihG #learning
Neural Variational Inference and Learning in Belief Networks (AM, KG), pp. 1791–1799.
ICMLICML-c2-2014-PandeyD #learning
Learning by Stretching Deep Networks (GP, AD), pp. 1719–1727.
ICMLICML-c2-2014-SuGR #predict
Structured Prediction of Network Response (HS, AG, JR), pp. 442–450.
ICMLICML-c2-2014-WuCLY #behaviour #consistency #learning #predict #social
Learning the Consistent Behavior of Common Users for Target Node Prediction across Social Networks (SHW, HHC, KHL, PSY), pp. 298–306.
ICPRICPR-2014-AlvaroSB
Offline Features for Classifying Handwritten Math Symbols with Recurrent Neural Networks (FA, JAS, JMB), pp. 2944–2949.
ICPRICPR-2014-ByeonLB #2d #classification #using
Texture Classification Using 2D LSTM Networks (WB, ML, TMB), pp. 1144–1149.
ICPRICPR-2014-ChenWCN #detection #fault #modelling
Confusion Network Based Recurrent Neural Network Language Modeling for Chinese OCR Error Detection (JC, YW, HC, PN), pp. 1266–1271.
ICPRICPR-2014-DongPHLDJ #classification #using
Vehicle Type Classification Using Unsupervised Convolutional Neural Network (ZD, MP, YH, TL, YD, YJ), pp. 172–177.
ICPRICPR-2014-DuHZWD #case study #classification #design #online #recognition #using
A Study of Designing Compact Classifiers Using Deep Neural Networks for Online Handwritten Chinese Character Recognition (JD, JSH, BZ, SW, LRD), pp. 2950–2955.
ICPRICPR-2014-FuscoEM #data analysis #locality
Bad Data Analysis with Sparse Sensors for Leak Localisation in Water Distribution Networks (FF, BE, SM), pp. 3642–3647.
ICPRICPR-2014-GoldhammerDBGS
Pedestrian’s Trajectory Forecast in Public Traffic with Artificial Neural Networks (MG, KD, UB, AG, BS), pp. 4110–4115.
ICPRICPR-2014-HafemannOC #recognition #using
Forest Species Recognition Using Deep Convolutional Neural Networks (LGH, LSO, PRC), pp. 1103–1107.
ICPRICPR-2014-HuangHWW #clustering
Deep Embedding Network for Clustering (PH, YH, WW, LW), pp. 1532–1537.
ICPRICPR-2014-HuangW0T #framework
A General Nonlinear Embedding Framework Based on Deep Neural Network (YH, WW, LW, TT), pp. 732–737.
ICPRICPR-2014-IosifidisTP #classification
Semi-supervised Classification of Human Actions Based on Neural Networks (AI, AT, IP), pp. 1336–1341.
ICPRICPR-2014-IwahoriFWB #image
Neural Network Based Image Modification for Shape from Observed SEM Images (YI, KF, RJW, MKB), pp. 2131–2136.
ICPRICPR-2014-KangKYLD #classification #documentation #image
Convolutional Neural Networks for Document Image Classification (LK, JK, PY, YL, DSD), pp. 3168–3172.
ICPRICPR-2014-KunwarPB #online #recognition
Semi-supervised Online Bayesian Network Learner for Handwritten Characters Recognition (RK, UP, MB), pp. 3104–3109.
ICPRICPR-2014-OGormanLY #multi #process
Creating a Unified, Wide-Area Activity Map for Multi-camera Networks (LO, DL, GY), pp. 4588–4593.
ICPRICPR-2014-RosaCJPFT #clustering #on the #using
On the Training of Artificial Neural Networks with Radial Basis Function Using Optimum-Path Forest Clustering (GHR, KAPC, LAPJ, JPP, AXF, JMRST), pp. 1472–1477.
ICPRICPR-2014-SunHJC #detection #image #robust
Robust Text Detection in Natural Scene Images by Generalized Color-Enhanced Contrasting Extremal Region and Neural Networks (LS, QH, WJ, KC), pp. 2715–2720.
ICPRICPR-2014-TsuchiyaMT
Exemplar Network: A Generalized Mixture Model (CT, TM, AT), pp. 598–603.
ICPRICPR-2014-Wilson #graph #modelling
Graph Signatures for Evaluating Network Models (RCW), pp. 100–105.
ICPRICPR-2014-WuHYWT #image #segmentation
Early Hierarchical Contexts Learned by Convolutional Networks for Image Segmentation (ZW, YH, YY, LW, TT), pp. 1538–1543.
ICPRICPR-2014-XuS #learning #using
Bayesian Network Structure Learning Using Causality (ZX, SNS), pp. 3546–3551.
KDDKDD-2014-00020GMB #community #on the
On the permanence of vertices in network communities (TC, SS, NG, AM, SB), pp. 1396–1405.
KDDKDD-2014-AkibaMK #analysis
Network structural analysis via core-tree-decomposition Publication of this article pending inquiry (TA, TM, KiK), pp. 1476–1485.
KDDKDD-2014-BensonRS #learning #multi #scalability
Learning multifractal structure in large networks (ARB, CR, SS), pp. 1326–1335.
KDDKDD-2014-DongYTYC #mobile #social
Inferring user demographics and social strategies in mobile social networks (YD, YY, JT, YY, NVC), pp. 15–24.
KDDKDD-2014-EmbarPB #framework
A bayesian framework for estimating properties of network diffusions (VRE, RKP, IB), pp. 1216–1225.
KDDKDD-2014-GhoshTLY #community #difference
The interplay between dynamics and networks: centrality, communities, and cheeger inequality (RG, SHT, KL, XY), pp. 1406–1415.
KDDKDD-2014-GuSJWC #estimation #topic
Topic-factorized ideal point estimation model for legislative voting network (YG, YS, NJ, BW, TC), pp. 183–192.
KDDKDD-2014-HerodotouDBOF #locality #realtime #scalability
Scalable near real-time failure localization of data center networks (HH, BD, SB, GO, PF), pp. 1689–1698.
KDDKDD-2014-JinKSDGCPR #community #geometry #modelling #social #using
Modeling mass protest adoption in social network communities using geometric brownian motion (FJ, RPK, NS, ERD, SG, FC, BAP, NR), pp. 1660–1669.
KDDKDD-2014-KhalilDS #optimisation #scalability
Scalable diffusion-aware optimization of network topology (EBK, BND, LS), pp. 1226–1235.
KDDKDD-2014-KurashimaITS #probability #visualisation
Probabilistic latent network visualization: inferring and embedding diffusion networks (TK, TI, NT, HS), pp. 1236–1245.
KDDKDD-2014-NiTFZ #ranking
Inside the atoms: ranking on a network of networks (JN, HT, WF, XZ), pp. 1356–1365.
KDDKDD-2014-PurohitPKZS #performance #scalability
Fast influence-based coarsening for large networks (MP, BAP, CK, YZ, VSS), pp. 1296–1305.
KDDKDD-2014-RozenshteinAGT #detection #process
Event detection in activity networks (PR, AA, AG, NT), pp. 1176–1185.
KDDKDD-2014-ShakarianSPB #social #source code
Reducing gang violence through network influence based targeting of social programs (PS, JS, WP, JB), pp. 1829–1836.
KDDKDD-2014-SintosT #social #using
Using strong triadic closure to characterize ties in social networks (SS, PT), pp. 1466–1475.
KDDKDD-2014-SpasojevicYRB #multi #named #scalability #social #topic
LASTA: large scale topic assignment on multiple social networks (NS, JY, AR, PB), pp. 1809–1818.
KDDKDD-2014-SunSTLKTY #behaviour #collaboration
Analyzing expert behaviors in collaborative networks (HS, MS, ST, YL, LMK, ST, XY), pp. 1486–1495.
KDDKDD-2014-WangHYL #multi #named
MMRate: inferring multi-aspect diffusion networks with multi-pattern cascades (SW, XH, PSY, ZL), pp. 1246–1255.
KDDKDD-2014-XiaoCT
Differentially private network data release via structural inference (QX, RC, KLT), pp. 911–920.
KDDKDD-2014-XuWCGSKDL #data mining #delivery #mining #social
Improving management of aquatic invasions by integrating shipping network, ecological, and environmental data: data mining for social good (JX, TLW, NVC, EKG, KS, RPK, JMD, DML), pp. 1699–1708.
KDDKDD-2014-ZhangCSWZ #probability #set #social
Minimizing seed set selection with probabilistic coverage guarantee in a social network (PZ, WC, XS, YW, JZ), pp. 1306–1315.
KDDKDD-2014-ZhangTMF #learning
Supervised deep learning with auxiliary networks (JZ, GT, YM, WF), pp. 353–361.
KDDKDD-2014-ZhouL #classification #mining #multi
Activity-edge centric multi-label classification for mining heterogeneous information networks (YZ, LL), pp. 1276–1285.
KDDKDD-2014-ZhuSY #analysis #mining #social
Network mining and analysis for social applications (FZ, HS, XY), p. 1974.
KDIRKDIR-2014-JohnsonC #clustering #identification
Mathematical Foundations of Networks Supporting Cluster Identification (JEJ, JWC), pp. 277–285.
KMISKMIS-2014-GuerrucciDAB #approach #information management
Phased Approach to a Knowledge Management Network (DG, RMD, RCA, DB), pp. 101–108.
KMISKMIS-2014-LambriniA14a #challenge #information management #ontology
Challenges and Directions for Knowledge Management in Networks of Aligned Ontologies (SL, KA), pp. 146–152.
KMISKMIS-2014-UrwinPCCPPY #ontology
Reference Ontologies for Global Production Networks (ENU, CP, AFCD, FSC, JMPS, SPG, RIMY), pp. 133–139.
KRKR-2014-BenferhatT #nondeterminism #reasoning
Reasoning with Uncertain Inputs in Possibilistic Networks (SB, KT).
MLDMMLDM-2014-FuMD #classification #multi #performance #towards
Towards the Efficient Recovery of General Multi-Dimensional Bayesian Network Classifier (SF, SM, MCD), pp. 16–30.
MLDMMLDM-2014-JavedA #classification #dataset #social #using
Creation of Bi-lingual Social Network Dataset Using Classifiers (IJ, HA), pp. 523–533.
MLDMMLDM-2014-OtteLK #memory management #pattern matching #pattern recognition #problem #recognition
Investigating Long Short-Term Memory Networks for Various Pattern Recognition Problems (SO, ML, DK), pp. 484–497.
MLDMMLDM-2014-SandovalH #learning #using
Learning of Natural Trading Strategies on Foreign Exchange High-Frequency Market Data Using Dynamic Bayesian Networks (JS, GH), pp. 408–421.
RecSysRecSys-2014-BhattacharyaZGGG #social #twitter
Inferring user interests in the Twitter social network (PB, MBZ, NG, SG, KPG), pp. 357–360.
RecSysRecSys-2014-GaoTL #personalisation #recommendation #social
Personalized location recommendation on location-based social networks (HG, JT, HL), pp. 399–400.
RecSysRecSys-2014-Vahedian #hybrid #recommendation
Weighted hybrid recommendation for heterogeneous networks (FV), pp. 429–432.
RecSysRecSys-2014-ZhangOFL #modelling #scalability #social
Scalable audience targeted models for brand advertising on social networks (KZ, AMO, SF, HL), pp. 341–344.
SEKESEKE-2014-CheMLC #online #protocol #runtime #testing
Testing Network Protocols: formally, at runtime and online (XC, SM, JL, ARC), pp. 90–93.
SEKESEKE-2014-JiangWZD #online #social
Forwarding Links without Browsing Links in Online Social Networks (JJ, XW, LZ, YD), pp. 636–641.
SEKESEKE-2014-WangGZ #predict #using
Artificial neural networks for infectious diarrhea prediction using meteorological factors in Shanghai (YW, JG, ZZ), pp. 501–506.
SIGIRSIGIR-2014-AnilSS #evolution #kernel #modelling #social #using
Modeling evolution of a social network using temporalgraph kernels (AA, NS, SRS), pp. 1051–1054.
SIGIRSIGIR-2014-BianYC #microblog #predict
Predicting trending messages and diffusion participants in microblogging network (JB, YY, TSC), pp. 537–546.
SIGIRSIGIR-2014-HuangTK #distance #people
The role of network distance in linkedin people search (SWH, DT, KK), pp. 867–870.
SIGIRSIGIR-2014-NguyenL #microblog #on the #predict
On predicting religion labels in microblogging networks (MTN, EPL), pp. 1211–1214.
SIGIRSIGIR-2014-Song #enterprise #multi #online #social #volunteer
Enrichment of user profiles across multiple online social networks for volunteerism matching for social enterprise (XS), p. 1282.
SKYSKY-2014-ExmanN #performance #recommendation #social
Location-based Fast Recommendation Social Network (IE, EN), pp. 55–62.
HILTHILT-2014-RathjeR #framework #java #model checking #source code
A framework for model checking UDP network programs with Java pathfinder (WR, BR), pp. 81–86.
PLDIPLDI-2014-BallBGIKSSV #named #source code #towards #verification
VeriCon: towards verifying controller programs in software-defined networks (TB, NB, AG, SI, AK, MS, MS, AV), p. 31.
POPLPOPL-2014-AndersonFGJKSW #named #semantics
NetkAT: semantic foundations for networks (CJA, NF, AG, JBJ, DK, CS, DW), pp. 113–126.
FSEFSE-2014-Lam #named #social
Omlet: a revolution against big-brother social networks (MSL), p. 1.
FSEFSE-2014-Yang #analysis #open source #overview #perspective #social
Social network analysis in open source software peer review (XY), pp. 820–822.
SACSAC-2014-A #authentication #health #mobile #privacy #research #student
Student research abstract: a privacy-preserving profile matching based authentication system for mobile health networks (DHA), pp. 196–197.
SACSAC-2014-BaeLKWP #privacy #social
Analyzing network privacy preserving methods: a perspective of social network characteristics (DHB, JML, SWK, YW, YP), pp. 331–332.
SACSAC-2014-BarddalGE #classification #concept #named #social
SFNClassifier: a scale-free social network method to handle concept drift (JPB, HMG, FE), pp. 786–791.
SACSAC-2014-ChoiKKLK
A new device discovery scheme in lighting control networks (SIC, SJK, IK, SKL, TGK), pp. 1743–1744.
SACSAC-2014-DhanjalC #learning
Learning reputation in an authorship network (CD, SC), pp. 1724–1726.
SACSAC-2014-FanC #approximate #framework #scalability #social
An approximate framework for scaling social influence computation in large networks (YCF, HC), pp. 610–615.
SACSAC-2014-GomesBM #similarity
A similarity model for virtual networks negotiation (RLG, LFB, ERMM), pp. 489–494.
SACSAC-2014-HuangLD #analysis #delivery #modelling #multi #performance
Modeling and analysis on network performance for cloud service delivery with multiple paths (JH, JL, QD), pp. 667–673.
SACSAC-2014-KimK #community #detection #mobile #social
A detection of overlapping community in mobile social network (PK, SK), pp. 175–179.
SACSAC-2014-KimKYP #online #social
Sampling in online social networks (SWK, KNK, SHY, SP), pp. 845–849.
SACSAC-2014-LiuCM #ad hoc #approach #composition #mobile
A low-latency service composition approach in mobile ad hoc networks (CL, JC, FLM), pp. 509–511.
SACSAC-2014-MazelFF #comparison #detection #diagrams #visual notation
Visual comparison of network anomaly detectors with chord diagrams (JM, RF, KF), pp. 473–480.
SACSAC-2014-ParkH #analysis #performance
Performance analysis of the golden-SM in the V2V network (MCP, DSH), pp. 1739–1740.
SACSAC-2014-ParkY #multi #simulation #smarttech
Encountering smartphones in network simulation: a preliminary result on multi-radio multicast (YP, WY), pp. 1727–1728.
SACSAC-2014-PengST #case study #development #mobile #social
Success factors in mobile social networking application development: case study of instagram (RP, DS, WTT), pp. 1072–1079.
SACSAC-2014-PessinOUWMV #evolution #learning #self
Self-localisation in indoor environments combining learning and evolution with wireless networks (GP, FSO, JU, DFW, RCM, PAV), pp. 661–666.
SACSAC-2014-RossiLR #algorithm #classification #using
A parameter-free label propagation algorithm using bipartite heterogeneous networks for text classification (RGR, AAL, SOR), pp. 79–84.
SACSAC-2014-SeffrinRJ #algebra
A dynamic bayesian network for inference of learners’ algebraic knowledge (HMS, GLR, PAJ), pp. 235–240.
SACSAC-2014-SeoKTC #analysis #framework #social
A structural analysis of literary fictions with social network framework (JS, SHK, HT, HGC), pp. 634–640.
SACSAC-2014-TesfayHBO #architecture #communication
Cyber-secure communication architecture for active power distribution networks (TTT, JPH, JYLB, PO), pp. 545–552.
SACSAC-2014-WangW
Wavelength resources based lightpath-level active rerouting in all-optical WDM networks (SWW, CYW), pp. 495–500.
SACSAC-2014-WangWPSC #collaboration #process
A collaborative processes synchronization method with regards to system crashes and network failures (LW, AW, LFP, MvS, CC), pp. 1393–1398.
SACSAC-2014-WangZC #composition #energy #framework
An energy-aware service composition framework for service-oriented wireless sensor networks (TW, KZ, LC), pp. 408–410.
SACSAC-2014-YoonY #authentication #using
A biometric-based authenticated key agreement scheme using ECC for wireless sensor networks (EJY, KYY), pp. 699–705.
SACSAC-2014-ZeilemakerSP #scalability
Large-scale message synchronization in challenged networks (NZ, BS, JAP), pp. 481–488.
CASECASE-2014-ChenLY #distributed #modelling
Sparse particle filtering for modeling space-time dynamics in distributed sensor networks (YC, GL, HY), pp. 626–631.
CASECASE-2014-HaoLGC #effectiveness #flexibility #markov #nondeterminism #problem #scheduling
An effective Markov network based EDA for flexible job shop scheduling problems under uncertainty (XCH, LL, MG, CFC), pp. 131–136.
CASECASE-2014-LinHC #gesture #recognition #using
Human hand gesture recognition using a convolution neural network (HIL, MHH, WKC), pp. 1038–1043.
CASECASE-2014-YenLLKWH #in the cloud #industrial
Advanced manufacturing solution to industry 4.0 trend through sensing network and Cloud Computing technologies (CTY, YCL, CCL, CCK, WBW, YRH), pp. 1150–1152.
DACDAC-2014-AdirGGS #generative #testing #using
Using a High-Level Test Generation Expert System for Testing In-Car Networks (AA, AG, LG, TS), p. 6.
DACDAC-2014-AxerTED #bound #performance
Exploiting Shaper Context to Improve Performance Bounds of Ethernet AVB Networks (PA, DT, RE, JD), p. 6.
DACDAC-2014-HuangYST #assessment #grid #power management
Physics-based Electromigration Assessment for Power Grid Networks (XH, TY, VS, SXDT), p. 6.
DACDAC-2014-HuWTT #hardware #monitoring #security
System-Level Security for Network Processors with Hardware Monitors (KH, TW, TT, RT), p. 6.
DACDAC-2014-LiangC #analysis #clustering #named #probability #reduction #scalability #smarttech
ClusRed: Clustering and Network Reduction Based Probabilistic Optimal Power Flow Analysis for Large-Scale Smart Grids (YL, DC), p. 6.
DACDAC-2014-QuintonBHNNE #analysis #design
Typical Worst Case Response-Time Analysis and its Use in Automotive Network Design (SQ, TTB, JH, MN, MN, RE), p. 6.
DACDAC-2014-RenMRZ #fault tolerance #using
Fault-tolerant Routing for On-chip Network Without Using Virtual Channels (PR, QM, XR, NZ), p. 6.
DACDAC-2014-ZhuangWLC #distributed #framework #named #simulation
MATEX: A Distributed Framework for Transient Simulation of Power Distribution Networks (HZ, SHW, JHL, CKC), p. 6.
DATEDATE-2014-0002LLCXY #big data #data analysis #energy #performance
Energy efficient neural networks for big data analytics (YW, BL, RL, YC, NX, HY), pp. 1–2.
DATEDATE-2014-BahrebarS #approach
Improving hamiltonian-based routing methods for on-chip networks: A turn model approach (PB, DS), pp. 1–4.
DATEDATE-2014-BaiS
Isochronous networks by construction (YB, KS), pp. 1–6.
DATEDATE-2014-BanagaayaAST #order #reduction
Implicit index-aware model order reduction for RLC/RC networks (NB, GA, WHAS, CT), pp. 1–6.
DATEDATE-2014-CasamassimaFB #power management
Context aware power management for motion-sensing body area network nodes (FC, EF, LB), pp. 1–6.
DATEDATE-2014-Huang14a #manycore #performance #predict
Leveraging on-chip networks for efficient prediction on multicore coherence (LH), pp. 1–4.
DATEDATE-2014-KordesVDW #detection #fault #hybrid #robust
Startup error detection and containment to improve the robustness of hybrid FlexRay networks (AK, BV, AKD, MGW), pp. 1–6.
DATEDATE-2014-MaliukM #framework #prototype
An analog non-volatile neural network platform for prototyping RF BIST solutions (DM, YM), pp. 1–6.
DATEDATE-2014-MottaghiRD #framework #named #performance
RETLab: A fast design-automation framework for arbitrary RET networks (MDM, AR, CD), pp. 1–6.
DATEDATE-2014-SarmaD #estimation #runtime
Minimal sparse observability of complex networks: Application to MPSoC sensor placement and run-time thermal estimation & tracking (SS, ND), pp. 1–6.
DATEDATE-2014-SeylerSWSGT #self
A self-propagating wakeup mechanism for point-to-point networks with partial network support (JRS, TS, JW, MS, MG, JT), pp. 1–6.
DATEDATE-2014-VillenaS #analysis #performance #variability
Efficient analysis of variability impact on interconnect lines and resistor networks (JFV, LMS), pp. 1–6.
DATEDATE-2014-WettinMKYPH #evaluation #performance
Performance evaluation of wireless NoCs in presence of irregular network routing strategies (PW, JM, RK, XY, PPP, DHH), pp. 1–6.
DATEDATE-2014-ZhangAJC #manycore
Thermal management of manycore systems with silicon-photonic networks (TZ, JLA, AJ, AKC), pp. 1–6.
DATEDATE-2014-ZygmontowiczDCP
Making it harder to unlock an LSIB: Honeytraps and misdirection in a P1687 network (AZ, JD, AC, JCP), pp. 1–6.
HPCAHPCA-2014-AnsariMXT #energy #named
Tangle: Route-oriented dynamic voltage minimization for variation-afflicted, energy-efficient on-chip networks (AA, AKM, JX, JT), pp. 440–451.
HPCAHPCA-2014-WonCGHS #learning #online #power management
Up by their bootstraps: Online learning in Artificial Neural Networks for CMP uncore power management (JYW, XC, PG, JH, VS), pp. 308–319.
HPDCHPDC-2014-ChenZJ #community #locality #named #online #performance #social
CBL: exploiting community based locality for efficient content search in online social networks (HC, FZ, HJ), pp. 299–304.
HPDCHPDC-2014-MuCWZ #replication #state machine
When paxos meets erasure code: reduce network and storage cost in state machine replication (SM, KC, YW, WZ), pp. 61–72.
HPDCHPDC-2014-PrisacariRHCMH #nearest neighbour #performance
Efficient task placement and routing of nearest neighbor exchanges in dragonfly networks (BP, GR, PH, DC, CM, TH), pp. 129–140.
OSDIOSDI-2014-KimHZHWWS #abstraction #gpu #named #source code
GPUnet: Networking Abstractions for GPU Programs (SK, SH, XZ, YH, AW, EW, MS), pp. 201–216.
PDPPDP-2014-BenzaidSB #protocol
An Enhanced Secure Pairwise Broadcast Time Synchronization Protocol in Wireless Sensor Networks (CB, AS, NB), pp. 569–573.
PDPPDP-2014-DamP #independence #process
Location Independent Routing in Process Network Overlays (MD, KP), pp. 715–724.
PDPPDP-2014-GhazelS #novel
A Novel QoS-Aware Method Based on Resource Control and Management in NGN Networks (CG, LAS), pp. 288–291.
PDPPDP-2014-GogolevM #classification #random
Density Classification in Asynchronous Random Networks with Faulty Nodes (AG, LM), pp. 256–261.
PDPPDP-2014-Hadim #communication #concurrent #multi #performance
The Multi-level Communication: Minimal Deadlock-Free and Storage Efficient Routing for Torus Networks (MBH), pp. 44–51.
PDPPDP-2014-MinarolliF #distributed #resource management #virtual machine
Distributed Resource Allocation to Virtual Machines via Artificial Neural Networks (DM, BF), pp. 490–499.
PDPPDP-2014-ShalabyRGFK #communication
Hierarchical Network Coding for Collective Communication on HPC Interconnects (AS, MESR, VG, IF, MK), pp. 98–102.
PDPPDP-2014-ZlydarevaMMOO #clustering
Event-Oriented Focal Weight-Based Clustering for Environmental Wireless Sensor Networks (OZ, BFM, WGM, JJO, GMPO), pp. 170–173.
ESOPESOP-2014-Garnock-JonesTF
The Network as a Language Construct (TGJ, STH, MF), pp. 473–492.
FASEFASE-2014-FiadeiroL
Heterogeneous and Asynchronous Networks of Timed Systems (JLF, AL), pp. 79–93.
FoSSaCSFoSSaCS-2014-BertrandFS #configuration management #game studies
Playing with Probabilities in Reconfigurable Broadcast Networks (NB, PF, AS), pp. 134–148.
STOCSTOC-2014-EneV #algorithm #approximate #bound #design #problem #requirements
Improved approximation algorithms for degree-bounded network design problems with node connectivity requirements (AE, AV), pp. 754–763.
STOCSTOC-2014-KrishnaswamyNPS #approximate #clustering #design #energy #performance
Cluster before you hallucinate: approximating node-capacitated network design and energy efficient routing (RK, VN, KP, CS), pp. 734–743.
TACASTACAS-2014-HerreraWP #query #reduction
Quasi-Equal Clock Reduction: More Networks, More Queries (CH, BW, AP), pp. 295–309.
WRLAWRLA-2014-LiuOM #ad hoc #framework #maude #mobile #realtime
A Framework for Mobile Ad hoc Networks in Real-Time Maude (SL, PCÖ, JM), pp. 162–177.
CAVCAV-2014-HuangFMMK #automaton #hybrid #invariant #verification
Invariant Verification of Nonlinear Hybrid Automata Networks of Cardiac Cells (ZH, CF, AM, SM, MZK), pp. 373–390.
ISSTAISSTA-2014-GotliebM #named #reduction #testing
FLOWER: optimal test suite reduction as a network maximum flow (AG, DM), pp. 171–180.
VMCAIVMCAI-2014-AcunaAMS #approach #complexity #heuristic #modelling
Modeling Parsimonious Putative Regulatory Networks: Complexity and Heuristic Approach (VA, AA, AM, AS), pp. 322–336.
HTHT-2013-HelicSGS #distributed #modelling #navigation
Models of human navigation in information networks based on decentralized search (DH, MS, MG, RS), pp. 89–98.
HTHT-2013-JarukasemratanaML #algorithm #community #detection #distance
Community detection algorithm based on centrality and node distance in scale-free networks (SJ, TM, XL), pp. 258–262.
HTHT-2013-KangL #information management #social
Structural and cognitive bottlenecks to information access in social networks (JHK, KL), pp. 51–59.
ICDARICDAR-2013-AlvaroSB #classification #hybrid #online
Classification of On-Line Mathematical Symbols with Hybrid Features and Recurrent Neural Networks (FA, JAS, JMB), pp. 1012–1016.
ICDARICDAR-2013-BlucheNK #feature model #recognition #word
Feature Extraction with Convolutional Neural Networks for Handwritten Word Recognition (TB, HN, CK), pp. 285–289.
ICDARICDAR-2013-BreuelUAS #using
High-Performance OCR for Printed English and Fraktur Using LSTM Networks (TMB, AUH, MIAAA, FS), pp. 683–687.
ICDARICDAR-2013-NalisnickB #sentiment
Extracting Sentiment Networks from Shakespeare’s Plays (ETN, HSB), pp. 758–762.
ICDARICDAR-2013-PuriST #learning
Bayesian Network Structure Learning and Inference Methods for Handwriting (MP, SNS, YT), pp. 1320–1324.
ICDARICDAR-2013-SchambachR #learning #sequence
Stabilize Sequence Learning with Recurrent Neural Networks by Forced Alignment (MPS, SFR), pp. 1270–1274.
ICDARICDAR-2013-Ul-HasanARSB #bidirectional #recognition
Offline Printed Urdu Nastaleeq Script Recognition with Bidirectional LSTM Networks (AUH, SBA, SFR, FS, TMB), pp. 1061–1065.
JCDLJCDL-2013-SuboticRS #distributed
A distributed archival network for process-oriented autonomic long-term digital preservation (IS, LR, HS), pp. 29–38.
SIGMODSIGMOD-2013-AkibaIY #distance #performance #query #scalability
Fast exact shortest-path distance queries on large networks by pruned landmark labeling (TA, YI, YY), pp. 349–360.
SIGMODSIGMOD-2013-HungBTCZ #named #query #scalability #visual notation
QUBLE: blending visual subgraph query formulation with query processing on large networks (HHH, SSB, BQT, BC, SZ), pp. 1097–1100.
SIGMODSIGMOD-2013-KhuranaD #named #scalability
HiNGE: enabling temporal network analytics at scale (UK, AD), pp. 1089–1092.
SIGMODSIGMOD-2013-MoustafaMDG #analysis #declarative #interactive #named
GRDB: a system for declarative and interactive analysis of noisy information networks (WEM, HM, AD, LG), pp. 1085–1088.
SIGMODSIGMOD-2013-TaoYLBCHKSWWW #analysis #named #research
Research-insight: providing insight on research by publication network analysis (FT, XY, KHL, GB, XC, JH, RK, YS, CW, LW, TW), pp. 1093–1096.
SIGMODSIGMOD-2013-VianaM #named #realtime #social
FriendRouter: real-time path finder in social networks (WV, MMM), pp. 1281–1282.
SIGMODSIGMOD-2013-ZhuMXLTZ #distance #query #theory and practice #towards
Shortest path and distance queries on road networks: towards bridging theory and practice (ADZ, HM, XX, SL, YT, SZ), pp. 857–868.
TPDLTPDL-2013-ReinandaUSR
Entity Network Extraction Based on Association Finding and Relation Extraction (RR, MU, FS, MdR), pp. 156–167.
VLDBVLDB-2013-GionisJLSW #social
Piggybacking on Social Networks (AG, FJ, VL, MS, IW), pp. 409–420.
VLDBVLDB-2013-HendawiBM #framework #named #predict #query #scalability
iRoad: A Framework For Scalable Predictive Query Processing On Road Networks (AMH, JB, MFM), pp. 1262–1265.
VLDBVLDB-2013-HuangCLQY #scalability
Top-K Structural Diversity Search in Large Networks (XH, HC, RHL, LQ, JXY), pp. 1618–1629.
VLDBVLDB-2013-MokbelS #data transformation #perspective #social
Mobility and Social Networking: A Data Management Perspective (MFM, MS), pp. 1196–1197.
VLDBVLDB-2014-BudakGAA13 #detection #named #online #roadmap #social
GeoScope: Online Detection of Geo-Correlated Information Trends in Social Networks (CB, TG, DA, AEA), pp. 229–240.
ITiCSEITiCSE-2013-FeasterAZH #algorithm #education #protocol
Serious toys II: teaching networks, protocols, and algorithms (YF, FA, JZ, JOH), pp. 273–278.
ITiCSEITiCSE-2013-QianYGBT #authentication #learning #mobile #security
Mobile device based authentic learning for computer network and security (KQ, MY, MG, PB, LT), p. 335.
ITiCSEITiCSE-2013-TrabelsiA #education #generative #using
Using network packet generators and snort rules for teaching denial of service attacks (ZT, LA), pp. 285–290.
CSMRCSMR-2013-BorgPR
Analyzing Networks of Issue Reports (MB, DP, PR), pp. 79–88.
CSMRCSMR-2013-SurianTLCL #predict
Predicting Project Outcome Leveraging Socio-Technical Network Patterns (DS, YT, DL, HC, EPL), pp. 47–56.
CSMRCSMR-2013-ThungBLJ #git #social
Network Structure of Social Coding in GitHub (FT, TFB, DL, LJ), pp. 323–326.
MSRMSR-2013-MacLeanK #commit #dataset #social
Apache commits: social network dataset (ACM, CDK), pp. 135–138.
MSRMSR-2013-WagstromJS #dataset #graph #ruby
A network of rails: a graph dataset of ruby on rails and associated projects (PW, CJ, AS), pp. 229–232.
ICALPICALP-v2-2013-HenzingerKN #maintenance
Sublinear-Time Maintenance of Breadth-First Spanning Tree in Partially Dynamic Networks (MH, SK, DN), pp. 607–619.
ICALPICALP-v2-2013-JurdzinskiKS #distributed
Distributed Deterministic Broadcasting in Wireless Networks of Weak Devices (TJ, DRK, GS), pp. 632–644.
ICALPICALP-v2-2013-Kleinberg #algorithm #social
Algorithms, Networks, and Social Phenomena (JMK), pp. 1–3.
ICALPICALP-v2-2013-MertziosMCS #constraints #optimisation
Temporal Network Optimization Subject to Connectivity Constraints (GBM, OM, IC, PGS), pp. 657–668.
ICALPICALP-v2-2013-MertziosS #bound #evolution
Strong Bounds for Evolution in Networks (GBM, PGS), pp. 669–680.
LATALATA-2013-DelzannoT #complexity #decidability #verification
Decidability and Complexity Results for Verification of Asynchronous Broadcast Networks (GD, RT), pp. 238–249.
CoGCIG-2013-Brown #graph #multi #search-based
Examination of graphs in Multiple Agent Genetic Networks for Iterated Prisoner's Dilemma (JAB), pp. 1–8.
CoGCIG-2013-Harrell #case study #modelling #social #using
Modeling player preferences in avatar customization using social network data: A case-study using virtual items in Team Fortress 2 (CUL, DFH), pp. 1–8.
DiGRADiGRA-2013-StokesWFW #collaboration #game studies
A Reality Game to Cross Disciplines: Fostering Networks and Collaboration (BS, JW, TF, SW).
FDGFDG-2013-KoutnikCSG #evolution #scalability
Evolving large-scale neural networks for vision-based TORCS (JK, GC, JS, FJG), pp. 206–212.
FDGFDG-2013-RyanSVC #education #game studies #security #using
Network Nightmares: Using games to teach networks and security (WR, JS, DV, JC), pp. 413–416.
CHICHI-2013-BernsteinBBK #social
Quantifying the invisible audience in social networks (MSB, EB, MB, BK), pp. 21–30.
CHICHI-2013-BrownMR #interactive #multi #named
MultiNet: reducing interaction overhead in domestic wireless networks (AB, RM, TR), pp. 1569–1578.
CHICHI-2013-CurmiFSW13a #named #social
HeartLink: open broadcast of live biometric data to social networks (FC, MAF, JS, JW), pp. 1749–1758.
CHICHI-2013-DunneS #clique #readability #visualisation
Motif simplification: improving network visualization readability with fan, connector, and clique glyphs (CD, BS), pp. 3247–3256.
CHICHI-2013-HillmanNPO #quote #social
“Shared joy is double joy”: the social practices of user networks within group shopping sites (SH, CN, CP, EO), pp. 2417–2426.
CHICHI-2013-HongYKAA #independence #social #using
Investigating the use of circles in social networks to support independence of individuals with autism (HH, SY, JGK, GDA, RIA), pp. 3207–3216.
CHICHI-2013-HowleyN #community
Factors impacting community response in an interest-sharing network (IH, TN), pp. 2283–2286.
CHICHI-2013-ShiXC #bound #social #using
Using contextual integrity to examine interpersonal information boundary on social network sites (PS, HX, YC), pp. 35–38.
CHICHI-2013-SpiliotopoulosO #comprehension #facebook #metric #privacy
Understanding motivations for facebook use: usage metrics, network structure, and privacy (TS, IO), pp. 3287–3296.
CSCWCSCW-2013-BradyZMB #social
Investigating the appropriateness of social network question asking as a resource for blind users (ELB, YZ, MRM, JPB), pp. 1225–1236.
CSCWCSCW-2013-BurtMB
Path dependent network advantage (RSB, JLM, JGB), pp. 1–2.
CSCWCSCW-2013-GaoHZ #how #social
Closure vs. structural holes: how social network information and culture affect choice of collaborators (GG, PJH, CZ), pp. 5–18.
CSCWCSCW-2013-GopalakrishnanHB #distributed #similarity
Voluntary turnover in a distributed work setting: an examination of the role of spatial propinquity and role similarity in project affiliation networks (GMG, DSH, SPB), pp. 329–340.
CSCWCSCW-2013-Heck #social
Combining social information for academic networking (TH), pp. 1387–1398.
CSCWCSCW-2013-LinF #learning
Opportunities via extended networks for teens’ informal learning (PL, SDF), pp. 1341–1352.
CSCWCSCW-2013-RobsonHKP #social #using
Comparing the use of social networking and traditional media channels for promoting citizen science (CR, MAH, CK, JSP), pp. 1463–1468.
HCIDUXU-CXC-2013-Said #social #using
Young Egyptians Use of Social Networks and the January 2011 Revolution (GRES), pp. 38–43.
HCIDUXU-NTE-2013-Gyoda #ad hoc #analysis #communication #evaluation #performance
Analysis and Evaluation of Wireless Ad Hoc Network Performance for a Disaster Communication Model and Scenarios (KG), pp. 65–74.
HCIDUXU-PMT-2013-ChiuT #design #effectiveness #idea #process #social
User Involvement in Idea Brainstorming of Design Process: Finding the Effective Strategy in Social Network Service (SCC, KT), pp. 593–598.
HCIDUXU-PMT-2013-KarahasanovicF #2d #approach #behaviour #experience #modelling
Modelling User Behaviour and Experience — The R2D2 Networks Approach (AK, AF), pp. 506–515.
HCIDUXU-PMT-2013-Moallem
Location, Location, Location: About Home Networking Devices Location and Features (AM), pp. 107–114.
HCIDUXU-WM-2013-Gould
Dot, Line, Network: Helping Individuals Make Sense of “New Data” (EWG), pp. 496–505.
HCIDUXU-WM-2013-Velez-RojasMRGK #enterprise #visualisation
Looking beyond the Single Pane of Glass: Visualization and Perspective in Enterprise Network (MCVR, SM, MR, SG, EK), pp. 581–590.
HCIHCI-AS-2013-FrajhofACLLM #collaboration #framework #learning #platform #social #student #usability
Usability of a Social Network as a Collaborative Learning Platform Tool for Medical Students (LF, ACCA, ATdSC, CJPdL, CAPdL, CRM), pp. 370–375.
HCIHCI-AS-2013-TavaresMFM #case study #experience
Experiences with Arthron for Live Surgery Transmission in Brazilian Telemedicine University Network (TAT, GHMBM, GLdSF, EM), pp. 197–206.
HCIHCI-III-2013-FernandesFMPB #framework
RFID Mesh Network as an Infrastructure for Location Based Services for the Blind (HF, JF, PM, HP, JB), pp. 39–45.
HCIHCI-IMT-2013-HenschenL #design #interface
A Web-Based Interface for a System That Designs Sensor Networks (LJH, JCL), pp. 688–697.
HCIHCI-UC-2013-BelliniBNP #recommendation
A Static and Dynamic Recommendations System for Best Practice Networks (PB, IB, PN, MP), pp. 259–268.
HCIHCI-UC-2013-GuercioMBL #education #experience #named #social
SOCIETY: A Social Reading Application to Join Education and Social Network Experience (EG, FLM, MB, LL), pp. 277–284.
HCIHCI-UC-2013-HayashiKO #empirical #social #trust
An Empirical Investigation of Similarity-Driven Trust Dynamics in a Social Network (YH, VVK, HO), pp. 20–28.
HCIHCI-UC-2013-HermannSTKS #concept #distributed #multi #social #user interface
The di.me User Interface: Concepts for Sharing Personal Information via Multiple Identities in a Decentralized Social Network (FH, AS, ST, CK, SS), pp. 29–38.
HCIHCI-UC-2013-KuramochiOTHN #analysis #community #graph #semantics #twitter #using
Applying to Twitter Networks of a Community Extraction Method Using Intersection Graph and Semantic Analysis (TK, NO, KT, YH, SN), pp. 314–323.
HCIHCI-UC-2013-TsaiHCL #communication #named #online #product line #social #using
Memotree: Using Online Social Networking to Strengthen Family Communication (THT, YLH, HTC, YWL), pp. 359–367.
HCIHIMI-D-2013-Maeshiro #multi
A Model of Living Organisms to Integrate Multiple Relationship Network Descriptions (TM), pp. 475–483.
HCIHIMI-D-2013-YanagimotoSY #classification #estimation #sentiment #using #word
Word Classification for Sentiment Polarity Estimation Using Neural Network (HY, MS, AY), pp. 669–677.
HCIHIMI-HSM-2013-ByerD #mobile
BARMOTIN- A Voice Controlled Mobile Tourism Information Network for Barbados (DB, CD), pp. 347–354.
HCIHIMI-HSM-2013-ChenHCK #design #social #usability
Usability Study of Icon Designs with Social Network Functions (CHC, WHH, SCC, YYK), pp. 355–362.
HCIHIMI-LCCB-2013-BoltonS #collaboration #design #education #social #tool support
Social Networking and Culturally Situated Design Teaching Tools: Providing a Collaborative Environment for K-12 (ATB, CDS), pp. 3–8.
HCIOCSC-2013-AdlerA #social
The Influence of Social Networking Sites on Participation in the 2012 Presidential Election (RFA, WDA), pp. 233–239.
HCIOCSC-2013-AdnanHAT #analysis #behaviour #eye tracking #online #social
Eye Tracking Analysis of User Behavior in Online Social Networks (WAWA, WNHH, NA, JT), pp. 113–119.
HCIOCSC-2013-BramanVDWRT #education #perspective #social
Teaching about the Impacts of Social Networks: An End of Life Perspective (JB, GV, AD, YW, KR, UT), pp. 240–249.
HCIOCSC-2013-Eustace #learning
Building and Sustaining a Lifelong Adult Learning Network (KE), pp. 260–268.
HCIOCSC-2013-FardounAC #community #education #online #social #student
Improvement of Students Curricula in Educational Environments by Means of Online Communities and Social Networks (HMF, AHA, APC), pp. 147–155.
HCIOCSC-2013-JiangB #approach #case study #multi #social
A Three-Level Approach to the Study of Multi-cultural Social Networking (YJ, OdB), pp. 365–374.
HCIOCSC-2013-LambropoulosTKM #community #framework #platform #semantics
Composites Ideas in COMPOOL Immersion: A Semantics Engineering Innovation Network Community Platform (NL, PT, IK, IM), pp. 385–394.
HCIOCSC-2013-LeeKCC #case study #facebook #online #social
Exploratory Study on Online Social Networks User from SASANG Constitution-Focused on Korean Facebook Users (JYL, HSK, EJC, SJC), pp. 58–66.
HCIOCSC-2013-Meiselwitz #assessment #policy #readability #social
Readability Assessment of Policies and Procedures of Social Networking Sites (GM), pp. 67–75.
HCIOCSC-2013-ShiYH #comprehension #matter #motivation #social
Understanding Social Network Sites (SNSs) Preferences: Personality, Motivation, and Happiness Matters (YS, XY, JH), pp. 94–103.
HCIOCSC-2013-WangA #social #using
Adult Learners and Their Use of Social Networking Sites (Y(W, JA), pp. 222–229.
HCIOCSC-2013-YueSC #social
Who Are Seeking Friends? The Portrait of Stranger-Seeker in Social Network Sites (XY, YS, HC), pp. 120–125.
CAiSECAiSE-2013-AgtK #automation #modelling #scalability #semantics
Automated Construction of a Large Semantic Network of Related Terms for Domain-Specific Modeling (HA, RDK), pp. 610–625.
EDOCEDOC-2013-BraunE #concept #online #overview #risk management #social #towards
Towards a Conceptualization of Corporate Risks in Online Social Networks: A Literature Based Overview of Risks (RB, WE), pp. 267–274.
ICEISICEIS-v1-2013-BarbosaCRM #estimation
Average Speed Estimation for Road Networks based on GPS Raw Trajectories (IB, MAC, CR, JAFdM), pp. 490–497.
ICEISICEIS-v1-2013-CoelhoAABB #industrial
Deploying Nodes for Industrial Wireless Networks by Artificial Immune Systems Techniques (PHGC, JLMdA, JFMdA, LFdAB, AVdB), pp. 536–540.
ICEISICEIS-v1-2013-Santibanez-GonzalezM #novel
A Novel Mathematical Formulation for the Strategic Planning of a Reverse Supply Chain Network — Theoretical and Computational Results (EDRSG, NM), pp. 570–577.
ICEISICEIS-v2-2013-LiL #agile #object-oriented #predict #process #using
Predicting Fault-proneness of Object-Oriented System Developed with Agile Process using Learned Bayesian Network (LL, HL), pp. 5–16.
ICEISICEIS-v2-2013-NascimentoVCS #ontology #social
Agent-based Electronic Commerce with Ontology Services and Social Network based Support (VN, MJV, AC, NS), pp. 497–504.
ICEISICEIS-v2-2013-ZhangWSP #linked data #open data #recommendation #social
Event Recommendation in Social Networks with Linked Data Enablement (YZ, HW, VSS, VKP), pp. 371–379.
ICEISICEIS-v3-2013-MalekanA #collaboration #modelling #process
Business Process Modeling Languages Supporting Collaborative Networks (HSM, HA), pp. 258–266.
ICEISICEIS-v3-2013-SzirbikB #architecture
Discovering the EIS Architecture that Supports Hub-and-Spoke Freight Transportation Networks Operating in a Cross Dock Mode (NBS, PB), pp. 388–395.
CIKMCIKM-2013-AkibaIY #linear #random #scalability
Linear-time enumeration of maximal K-edge-connected subgraphs in large networks by random contraction (TA, YI, YY), pp. 909–918.
CIKMCIKM-2013-ArifuzzamanKM #algorithm #named #parallel
PATRIC: a parallel algorithm for counting triangles in massive networks (SA, MK, MVM), pp. 529–538.
CIKMCIKM-2013-BarahmandGY #comparison #design #interactive #physics #social
A comparison of two physical data designs for interactive social networking actions (SB, SG, JY), pp. 949–958.
CIKMCIKM-2013-BogdanovS #effectiveness #nearest neighbour #scalability
Accurate and scalable nearest neighbors in large networks based on effective importance (PB, AKS), pp. 1009–1018.
CIKMCIKM-2013-ChenCC #query
Spatial-temporal query homogeneity for KNN object search on road networks (YJC, KTC, MSC), pp. 1019–1028.
CIKMCIKM-2013-FerenceYL #recommendation #social
Location recommendation for out-of-town users in location-based social networks (GF, MY, WCL), pp. 721–726.
CIKMCIKM-2013-GuoZZCG #personalisation #social
Personalized influence maximization on social networks (JG, PZ, CZ, YC, LG), pp. 199–208.
CIKMCIKM-2013-HashemiNB #approach #learning #retrieval #topic
Expertise retrieval in bibliographic network: a topic dominance learning approach (SHH, MN, HB), pp. 1117–1126.
CIKMCIKM-2013-KongZY #multi #social
Inferring anchor links across multiple heterogeneous social networks (XK, JZ, PSY), pp. 179–188.
CIKMCIKM-2013-LiMWLX #on the #online #predict #social
On popularity prediction of videos shared in online social networks (HL, XM, FW, JL, KX), pp. 169–178.
CIKMCIKM-2013-McDowellA #classification
Labels or attributes?: rethinking the neighbors for collective classification in sparsely-labeled networks (LM, DWA), pp. 847–852.
CIKMCIKM-2013-MengK
Discovering influential authors in heterogeneous academic networks by a co-ranking method (QM, PJK), pp. 1029–1036.
CIKMCIKM-2013-MirylenkaP #navigation #topic #wiki
Navigating the topical structure of academic search results via the Wikipedia category network (DM, AP), pp. 891–896.
CIKMCIKM-2013-MishraRT #predict #process
Estimating the relative utility of networks for predicting user activities (NM, DMR, PT), pp. 1047–1056.
CIKMCIKM-2013-RongM #social
Diffusion of innovations revisited: from social network to innovation network (XR, QM), pp. 499–508.
CIKMCIKM-2013-TanGC0Z #detection #named #social
UNIK: unsupervised social network spam detection (ET, LG, SC, XZ, YEZ), pp. 479–488.
CIKMCIKM-2013-WangYLZH #summary #topic #word
Content coverage maximization on word networks for hierarchical topic summarization (CW, XY, YL, CZ, JH), pp. 249–258.
CIKMCIKM-2013-ZhangDDC #probability #social
Probabilistic solutions of influence propagation on social networks (MZ, CD, CHQD, EC), pp. 429–438.
CIKMCIKM-2013-ZhaoLHCH #recommendation #social
Community-based user recommendation in uni-directional social networks (GZ, MLL, WH, WC, HH), pp. 189–198.
CIKMCIKM-2013-ZhongLTZ #named