BibSLEIGH — network tag

network

network

Tag #network

4800 papers:

POPL-2020-BeckettGMW #abstract interpretation #distributed: Abstract interpretation of distributed network control planes (RB, AG, RM, DW), p. 27.
ASPLOS-2020-TorkMS #architecture #named: Lynx: A SmartNIC-driven Accelerator-centric Architecture for Network Servers (MT, LM, MS), pp. 117–131.
CC-2020-KimLKS #robust: Robust quantization of deep neural networks (YK, JL, YK, JS), pp. 74–84.
CSL-2020-BielousK #game studies: Coverage and Vacuity in Network Formation Games (GB, OK), p. 18.
EDM-2019-HuR #estimation #graph #performance: Academic Performance Estimation with Attention-based Graph Convolutional Networks (QH, HR).
EDM-2019-KaserS #classification #interactive #modelling #student: Exploring Neural Network Models for the Classification of Students in Highly Interactive Environments (TK, DLS).
EDM-2019-TatoND #hybrid #predict #reasoning: Hybrid Deep Neural Networks to Predict Socio-Moral Reasoning Skills (AANT, RN, AD).
ICSME-2019-BraiekK #approach #named #search-based #testing: DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks (HBB, FK), pp. 454–458.
ICSME-2019-PalacioMMBPS #identification #learning #using: Learning to Identify Security-Related Issues Using Convolutional Neural Networks (DNP, DM, KM, CBC, DP, CS), pp. 140–144.
MSR-2019-GoteSS #git #mining #named #repository #scalability: git2net: mining time-stamped co-editing networks from large git repositories (CG, IS, FS), pp. 433–444.
MSR-2019-LiuLZFDQ #commit #generative #using: Generating commit messages from diffs using pointer-generator network (QL, ZL, HZ, HF, BD, YQ), pp. 299–309.
SANER-2019-NghiYJ #algorithm #classification #dependence: Bilateral Dependency Neural Networks for Cross-Language Algorithm Classification (BDQN, YY, LJ), pp. 422–433.
SANER-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.
FM-2019-SuP0 #scalability: Controlling Large Boolean Networks with Temporary and Permanent Perturbations (CS, SP, JP0), pp. 707–724.
FM-2019-TranLMYNXJ #analysis #reachability: Star-Based Reachability Analysis of Deep Neural Networks (HDT, DML, PM, XY, LVN, WX, TTJ), pp. 670–686.
AIIDE-2019-BontragerKASST #game studies #learning: “Superstition” in the Network: Deep Reinforcement Learning Plays Deceptive Games (PB, AK, DA, MS, CS, JT), pp. 10–16.
CoG-2019-GiacomelloLL #generative: Searching the Latent Space of a Generative Adversarial Network to Generate DOOM Levels (EG, PLL, DL), pp. 1–8.
CoG-2019-ParakhCS #approach #design #game studies #problem #towards: An Approach Towards Designing Problem Networks in Serious Games (AP, PC, MS), pp. 1–8.
CoG-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.
CoG-2019-Sovrano #experience #random: Combining Experience Replay with Exploration by Random Network Distillation (FS), pp. 1–8.
CoG-2019-TongLL #evolution #policy: Enhancing Rolling Horizon Evolution with Policy and Value Networks (XT, WL, BL0), pp. 1–8.
FDG-2019-HoC #community #visualisation: Roguelike ancestry network visualisation: insights from the roguelike community (XH, MC), p. 9.
FDG-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.
FDG-2019-MillerWK #evolution: Evolving unsupervised neural networks for Slither.io (MM, MW, FK), p. 5.
FDG-2019-SongW #evolution #generative #named: TownSim: agent-based city evolution for naturalistic road network generation (AS, JW), p. 9.
VS-Games-2019-BakalosRDDPV #identification #using: Choreographic Pose Identification using Convolutional Neural Networks (NB, IR, ND, AD, EP, AV), pp. 1–7.
CIKM-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.
CIKM-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.
CIKM-2019-BonchiGGOR #community: Discovering Polarized Communities in Signed Networks (FB, EG, AG, BO, GR), pp. 961–970.
CIKM-2019-CaiYZR: Adversarial Structured Neural Network Pruning (XC, JY, FZ, SR), pp. 2433–2436.
CIKM-2019-CaoCL0: Nested Relation Extraction with Iterative Neural Network (YC, DC0, HL0, PL0), pp. 1001–1010.
CIKM-2019-CaoDGMT #named #query: BeLink: Querying Networks of Facts, Statements and Beliefs (TDC, LD, FG, IM, XT), pp. 2941–2944.
CIKM-2019-CaoZSX #modelling #named #sequence: HiCAN: Hierarchical Convolutional Attention Network for Sequence Modeling (YC, WZ, BS, CX), pp. 1723–1732.
CIKM-2019-ChenCCR #recommendation: A Dynamic Co-attention Network for Session-based Recommendation (WC, FC, HC, MdR), pp. 1461–1470.
CIKM-2019-ChenLX0 #classification #sentiment: Sentiment Commonsense Induced Sequential Neural Networks for Sentiment Classification (SC, XL, YX, LH0), pp. 1021–1030.
CIKM-2019-ChenLYZS #graph: Knowledge-aware Textual Entailment with Graph Attention Network (DC, YL, MY0, HTZ, YS), pp. 2145–2148.
CIKM-2019-ChenSHG #detection: Attention-Residual Network with CNN for Rumor Detection (YC, JS, LH, WG), pp. 1121–1130.
CIKM-2019-ChenSTCS #performance #random: Fast and Accurate Network Embeddings via Very Sparse Random Projection (HC, SFS, YT, MC, SS), pp. 399–408.
CIKM-2019-DerrJCT: Balance in Signed Bipartite Networks (TD, CJ, YC, JT), pp. 1221–1230.
CIKM-2019-DongZHSL #detection #graph #multi: Multiple Rumor Source Detection with Graph Convolutional Networks (MD, BZ, NQVH, HS, GL), pp. 569–578.
CIKM-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.
CIKM-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.
CIKM-2019-FuL #estimation #named: DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation (TYF, WCL), pp. 69–78.
CIKM-2019-Gao0WL #bidirectional #interactive #recognition: Gate-based Bidirectional Interactive Decoding Network for Scene Text Recognition (YG, YC0, JW, HL), pp. 2273–2276.
CIKM-2019-GuLL #interactive #multi: Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots (JCG, ZHL, QL), pp. 2321–2324.
CIKM-2019-HeSLJPP #named #random: HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding (YH, YS, JL, CJ, JP, HP), pp. 639–648.
CIKM-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.
CIKM-2019-HuangSZWC #learning #self: Similarity-Aware Network Embedding with Self-Paced Learning (CH0, BS, XZ, XW, NVC), pp. 2113–2116.
CIKM-2019-HuangWWT #multi #named #self: DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting (SH, DW, XW, AT), pp. 2129–2132.
CIKM-2019-HuangWZLC #classification #prototype: Deep Prototypical Networks for Imbalanced Time Series Classification under Data Scarcity (CH0, XW, XZ, SL, NVC), pp. 2141–2144.
CIKM-2019-HuangZDB: Deep Dynamic Fusion Network for Traffic Accident Forecasting (CH, CZ, PD, LB), pp. 2673–2681.
CIKM-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.
CIKM-2019-JiaoXZZ #graph #predict: Collective Link Prediction Oriented Network Embedding with Hierarchical Graph Attention (YJ, YX, JZ, YZ), pp. 419–428.
CIKM-2019-KangT #mining #named: N2N: Network Derivative Mining (JK, HT), pp. 861–870.
CIKM-2019-Lakhotia0 #algorithm #approximate #coordination #social: Approximation Algorithms for Coordinating Ad Campaigns on Social Networks (KL, DK0), pp. 339–348.
CIKM-2019-LeePY #named: BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network (SL, CP, HY), pp. 619–628.
CIKM-2019-LeeRKKKR #graph: Graph Convolutional Networks with Motif-based Attention (JBL, RAR, XK, SK, EK, AR), pp. 499–508.
CIKM-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.
CIKM-2019-LiECL #clustering #identification #mobile #multi: Multi-scale Trajectory Clustering to Identify Corridors in Mobile Networks (LL, SME, CAC, CL), pp. 2253–2256.
CIKM-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.
CIKM-2019-LiHLDZ #detection #named: SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks (YL, XH, JL, MD, NZ), pp. 2233–2236.
CIKM-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.
CIKM-2019-LinPLO #recognition #using: An Explainable Deep Fusion Network for Affect Recognition Using Physiological Signals (JL, SP, CSL, SLO), pp. 2069–2072.
CIKM-2019-LinWXLB #estimation #hybrid #using: Path Travel Time Estimation using Attribute-related Hybrid Trajectories Network (XL, YW, XX, ZL, SSB), pp. 1973–1982.
CIKM-2019-LiQLYL #detection #graph #overview: Spam Review Detection with Graph Convolutional Networks (AL, ZQ, RL, YY, DL), pp. 2703–2711.
CIKM-2019-LiuZH #graph #representation #towards: Towards Explainable Representation of Time-Evolving Graphs via Spatial-Temporal Graph Attention Networks (ZL, DZ, JH), pp. 2137–2140.
CIKM-2019-LiY0X #component: Heterogeneous Components Fusion Network for Load Forecasting of Charging Stations (KL, FY, CF0, TX), pp. 2285–2288.
CIKM-2019-LiZWHYL #multi: Multi-Hot Compact Network Embedding (CL, LZ, SW, FH, PSY, ZL), pp. 459–468.
CIKM-2019-LongWDSJL #approach #community: Hierarchical Community Structure Preserving Network Embedding: A Subspace Approach (QL, YW, LD, GS, YJ, WL), pp. 409–418.
CIKM-2019-LuoCXQ #community: Best Co-Located Community Search in Attributed Networks (JL, XC, XX, QQ), pp. 2453–2456.
CIKM-2019-LuWSYY: Temporal Network Embedding with Micro- and Macro-dynamics (YL, XW0, CS, PSY, YY), pp. 469–478.
CIKM-2019-MauryaLM #approximate #graph #performance: Fast Approximations of Betweenness Centrality with Graph Neural Networks (SKM, XL0, TM), pp. 2149–2152.
CIKM-2019-PangWZG0 #design #generative #named: NAD: Neural Network Aided Design for Textile Pattern Generation (ZP, SW, DZ, YG, GC0), pp. 2081–2084.
CIKM-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.
CIKM-2019-ParkKZ0Y: Task-Guided Pair Embedding in Heterogeneous Network (CP, DK, QZ, JH0, HY), pp. 489–498.
CIKM-2019-PratamaZAO0 #automation #multi #streaming: Automatic Construction of Multi-layer Perceptron Network from Streaming Examples (MP, CZ, AA, YSO, WD0), pp. 1171–1180.
CIKM-2019-QiuLHY #graph #order #recommendation: Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks (RQ, JL, ZH, HY), pp. 579–588.
CIKM-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.
CIKM-2019-ShiLLP #multi: A Multi-Scale Temporal Feature Aggregation Convolutional Neural Network for Portfolio Management (SS, JL, GL, PP), pp. 1613–1622.
CIKM-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.
CIKM-2019-ShiY #analysis: Recent Developments of Deep Heterogeneous Information Network Analysis (CS, PSY), pp. 2973–2974.
CIKM-2019-ShiYZ #analysis: HENA 2019: The 3rd Workshop of Heterogeneous Information Network Analysis and Applications (CS, YY, JZ), pp. 2991–2992.
CIKM-2019-SongCZX #memory management #recommendation: Session-based Recommendation with Hierarchical Memory Networks (BS, YC, WZ, CX), pp. 2181–2184.
CIKM-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.
CIKM-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.
CIKM-2019-Wang0T00: Discerning Edge Influence for Network Embedding (YW, YY0, HT, FX0, JL0), pp. 429–438.
CIKM-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.
CIKM-2019-WangL #behaviour #learning: Spotting Terrorists by Learning Behavior-aware Heterogeneous Network Embedding (PCW, CTL), pp. 2097–2100.
CIKM-2019-WangLL #interactive #predict: Neighborhood Interaction Attention Network for Link Prediction (ZW, YL, WL), pp. 2153–2156.
CIKM-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.
CIKM-2019-WangWC #multi: Multi-Turn Response Selection in Retrieval-Based Chatbots with Iterated Attentive Convolution Matching Network (HW, ZW, JC), pp. 1081–1090.
CIKM-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.
CIKM-2019-WuH #scalability: Scalable Manifold-Regularized Attributed Network Embedding via Maximum Mean Discrepancy (JW, JH), pp. 2101–2104.
CIKM-2019-WuPDTZD #distance #graph #learning: Long-short Distance Aggregation Networks for Positive Unlabeled Graph Learning (MW, SP, LD, IWT, XZ, BD), pp. 2157–2160.
CIKM-2019-XiaoZZXBZY #3d #multi #recognition: Multi-view Moments Embedding Network for 3D Shape Recognition (JX, YZ, PZ, KX, KB, CZ, WY), pp. 2257–2260.
CIKM-2019-XuHY #graph #learning #scalability: Scalable Causal Graph Learning through a Deep Neural Network (CX, HH, SY), pp. 1853–1862.
CIKM-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.
CIKM-2019-YanCKWM #2d #named #recommendation: CosRec: 2D Convolutional Neural Networks for Sequential Recommendation (AY, SC, WCK, MW, JJM), pp. 2173–2176.
CIKM-2019-YangGWSX0 #summary: Query-Specific Knowledge Summarization with Entity Evolutionary Networks (CY, LG, ZW, JS, JX, JH0), pp. 2121–2124.
CIKM-2019-YangWCW #graph #predict #using: Using External Knowledge for Financial Event Prediction Based on Graph Neural Networks (YY, ZW, QC, LW), pp. 2161–2164.
CIKM-2019-YaoHGH #low level: Regularizing Deep Neural Networks by Ensemble-based Low-Level Sample-Variances Method (SY, YH, LG, ZH), pp. 1111–1120.
CIKM-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.
CIKM-2019-YouVLL #multi #recommendation: Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation (DY, NV, KL, QL), pp. 1471–1480.
CIKM-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.
CIKM-2019-ZhangRZYZ #named: PRNet: Outdoor Position Recovery for Heterogenous Telco Data by Deep Neural Network (YZ, WR, KZ, MY, JZ), pp. 1933–1942.
CIKM-2019-ZhaoZSL #recommendation: Motif Enhanced Recommendation over Heterogeneous Information Network (HZ, YZ, YS, DLL), pp. 2189–2192.
ECIR-p1-2019-FardBW #predict: Relationship Prediction in Dynamic Heterogeneous Information Networks (AMF, EB, KW0), pp. 19–34.
ECIR-p1-2019-Sanz-CruzadoC #information retrieval #modelling #recommendation #social: Information Retrieval Models for Contact Recommendation in Social Networks (JSC, PC), pp. 148–163.
ECIR-p1-2019-VoB: Extracting Temporal Event Relations Based on Event Networks (DTV, EB), pp. 844–851.
ECIR-p1-2019-ZhangJ #image #predict #twitter: Image Tweet Popularity Prediction with Convolutional Neural Network (YZ0, AJ), pp. 803–809.
ECIR-p2-2019-ImaniVMS19a #query #using #word: Deep Neural Networks for Query Expansion Using Word Embeddings (AI, AV, AM, AS), pp. 203–210.
ICML-2019-0002LA #multi: Exploring interpretable LSTM neural networks over multi-variable data (TG0, TL, NAF), pp. 2494–2504.
ICML-2019-AlbuquerqueMDCF #generative #multi: Multi-objective training of Generative Adversarial Networks with multiple discriminators (IA, JM, TD, BC, THF, IM), pp. 202–211.
ICML-2019-AletJVRLK #adaptation #graph #memory management: Graph Element Networks: adaptive, structured computation and memory (FA, AKJ, MBV, AR, TLP, LPK), pp. 212–222.
ICML-2019-AnconaOG #algorithm #approximate #polynomial: Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation (MA, CÖ, MHG), pp. 272–281.
ICML-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.
ICML-2019-BeckerPGZTN: Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces (PB, HP, GHWG, CZ, CJT, GN), pp. 544–552.
ICML-2019-BehrmannGCDJ: Invertible Residual Networks (JB, WG, RTQC, DD, JHJ), pp. 573–582.
ICML-2019-BiePC #probability: Stochastic Deep Networks (GdB, GP, MC), pp. 1556–1565.
ICML-2019-BiettiMCM #kernel: A Kernel Perspective for Regularizing Deep Neural Networks (AB, GM, DC, JM), pp. 664–674.
ICML-2019-CasadoM #constraints #orthogonal: Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group (MLC, DMR), pp. 3794–3803.
ICML-2019-ChattopadhyayMS #perspective: Neural Network Attributions: A Causal Perspective (AC, PM, AS, VNB), pp. 981–990.
ICML-2019-ChenLCZ #comprehension: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels (PC, BL, GC, SZ), pp. 1062–1070.
ICML-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.
ICML-2019-ChiquetRM #re-engineering: Variational Inference for sparse network reconstruction from count data (JC, SR, MM), pp. 1162–1171.
ICML-2019-CohenWKW: Gauge Equivariant Convolutional Networks and the Icosahedral CNN (TC, MW, BK, MW), pp. 1321–1330.
ICML-2019-DuH #linear #matter #optimisation: Width Provably Matters in Optimization for Deep Linear Neural Networks (SSD, WH), pp. 1655–1664.
ICML-2019-DuLL0Z: Gradient Descent Finds Global Minima of Deep Neural Networks (SSD, JDL, HL, LW0, XZ), pp. 1675–1685.
ICML-2019-DziedzicPKEF: Band-limited Training and Inference for Convolutional Neural Networks (AD, JP, SK, AJE, MJF), pp. 1745–1754.
ICML-2019-FischerBDGZV #logic #named #query: DL2: Training and Querying Neural Networks with Logic (MF, MB, DDC, TG, CZ, MTV), pp. 1931–1941.
ICML-2019-FranceschiNPH #graph #learning: Learning Discrete Structures for Graph Neural Networks (LF, MN, MP, XH), pp. 1972–1982.
ICML-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.
ICML-2019-GeifmanE #named: SelectiveNet: A Deep Neural Network with an Integrated Reject Option (YG, REY), pp. 2151–2159.
ICML-2019-GhaffariLM #algorithm #clustering #parallel: Improved Parallel Algorithms for Density-Based Network Clustering (MG, SL, SM), pp. 2201–2210.
ICML-2019-GoldfeldBGMNKP #data flow: Estimating Information Flow in Deep Neural Networks (ZG, EvdB, KHG, IM, NN, BK, YP), pp. 2299–2308.
ICML-2019-HaberLTR: IMEXnet A Forward Stable Deep Neural Network (EH, KL, ET, LR), pp. 2525–2534.
ICML-2019-HacohenW #education #learning #on the #power of: On The Power of Curriculum Learning in Training Deep Networks (GH, DW), pp. 2535–2544.
ICML-2019-HaninR #complexity #linear: Complexity of Linear Regions in Deep Networks (BH, DR), pp. 2596–2604.
ICML-2019-HavivRB #comprehension #memory management: Understanding and Controlling Memory in Recurrent Neural Networks (DH, AR, OB), pp. 2663–2671.
ICML-2019-HayouDR #on the: On the Impact of the Activation function on Deep Neural Networks Training (SH, AD, JR), pp. 2672–2680.
ICML-2019-HsiehLC #generative #nash: Finding Mixed Nash Equilibria of Generative Adversarial Networks (YPH, CL, VC), pp. 2810–2819.
ICML-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.
ICML-2019-JeongLK: Ladder Capsule Network (TJ, YL, HK), pp. 3071–3079.
ICML-2019-KayaHD #comprehension: Shallow-Deep Networks: Understanding and Mitigating Network Overthinking (YK, SH, TD), pp. 3301–3310.
ICML-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.
ICML-2019-KoLWDWL #named #robust: POPQORN: Quantifying Robustness of Recurrent Neural Networks (CYK, ZL, LW, LD, NW, DL), pp. 3468–3477.
ICML-2019-Kornblith0LH #revisited #similarity: Similarity of Neural Network Representations Revisited (SK, MN0, HL, GEH), pp. 3519–3529.
ICML-2019-Labatie: Characterizing Well-Behaved vs. Pathological Deep Neural Networks (AL), pp. 3611–3621.
ICML-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.
ICML-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.
ICML-2019-LiDMMHH #learning #named: LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning (HYL, WD, XM, CM, FH, BGH), pp. 3825–3834.
ICML-2019-LiGDVK #graph #learning #similarity: Graph Matching Networks for Learning the Similarity of Graph Structured Objects (YL, CG, TD, OV, PK), pp. 3835–3845.
ICML-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.
ICML-2019-LiYZH: Feature-Critic Networks for Heterogeneous Domain Generalization (YL, YY, WZ, TMH), pp. 3915–3924.
ICML-2019-Ma0KW0 #graph: Disentangled Graph Convolutional Networks (JM, PC0, KK, XW0, WZ0), pp. 4212–4221.
ICML-2019-MahoneyM #modelling: Traditional and Heavy Tailed Self Regularization in Neural Network Models (MWM, CM), pp. 4284–4293.
ICML-2019-MaronFSL #invariant #on the: On the Universality of Invariant Networks (HM, EF, NS, YL), pp. 4363–4371.
ICML-2019-MehtaCR #graph #probability: Stochastic Blockmodels meet Graph Neural Networks (NM, LC, PR), pp. 4466–4474.
ICML-2019-MellerFAG #fault: Same, Same But Different: Recovering Neural Network Quantization Error Through Weight Factorization (EM, AF, UA, MG), pp. 4486–4495.
ICML-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.
ICML-2019-MostafaW #parametricity #performance: Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization (HM, XW), pp. 4646–4655.
ICML-2019-NayakMSRC: Zero-Shot Knowledge Distillation in Deep Networks (GKN, KRM, VS, VBR, AC), pp. 4743–4751.
ICML-2019-NoklandE #fault: Training Neural Networks with Local Error Signals (AN, LHE), pp. 4839–4850.
ICML-2019-OdenaOAG #debugging #fuzzing #named: TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing (AO, CO, DA, IJG), pp. 4901–4911.
ICML-2019-OonoS #approximate #estimation #parametricity: Approximation and non-parametric estimation of ResNet-type convolutional neural networks (KO, TS), pp. 4922–4931.
ICML-2019-PanousisCT #contest #parametricity: Nonparametric Bayesian Deep Networks with Local Competition (KPP, SC, ST), pp. 4980–4988.
ICML-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.
ICML-2019-PengWCH #collaboration: Collaborative Channel Pruning for Deep Networks (HP, JW, SC, JH), pp. 5113–5122.
ICML-2019-QuBT #graph #markov #named: GMNN: Graph Markov Neural Networks (MQ, YB, JT0), pp. 5241–5250.
ICML-2019-RahamanBADLHBC #bias #on the: On the Spectral Bias of Neural Networks (NR, AB, DA, FD, ML, FAH, YB, ACC), pp. 5301–5310.
ICML-2019-RahmanJG #compilation: Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation (TR, SJ, VG), pp. 5311–5320.
ICML-2019-RatzlaffL #generative #named: HyperGAN: A Generative Model for Diverse, Performant Neural Networks (NR, FL), pp. 5361–5369.
ICML-2019-ShenHCD #independence #testing: Conditional Independence in Testing Bayesian Networks (YS, HH, AC, AD), pp. 5701–5709.
ICML-2019-ShiK0 #modelling #scalability: Scalable Training of Inference Networks for Gaussian-Process Models (JS, MEK, JZ0), pp. 5758–5768.
ICML-2019-Simon-GabrielOB #first-order: First-Order Adversarial Vulnerability of Neural Networks and Input Dimension (CJSG, YO, LB, BS, DLP), pp. 5809–5817.
ICML-2019-SimsekliSG #analysis #probability: A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks (US, LS, MG), pp. 5827–5837.
ICML-2019-SinghTJGB #generative #parametricity: Non-Parametric Priors For Generative Adversarial Networks (RS, PKT, SJ, RG, MWB), pp. 5838–5847.
ICML-2019-TaiBV: Equivariant Transformer Networks (KST, PB, GV), pp. 6086–6095.
ICML-2019-TanL #named #scalability: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (MT, QVL), pp. 6105–6114.
ICML-2019-TranKSK #named #using: DeepNose: Using artificial neural networks to represent the space of odorants (NBT, DRK, SS, AAK), pp. 6305–6314.
ICML-2019-TurnerHFSY #generative: Metropolis-Hastings Generative Adversarial Networks (RDT, JH, EF, YS, JY), pp. 6345–6353.
ICML-2019-VladimirovaVMA #comprehension: Understanding Priors in Bayesian Neural Networks at the Unit Level (MV, JV, PM, JA), pp. 6458–6467.
ICML-2019-Wang0XZ: Convolutional Poisson Gamma Belief Network (CW, BC0, SX, MZ), pp. 6515–6525.
ICML-2019-WangN: State-Regularized Recurrent Neural Networks (CW, MN), pp. 6596–6606.
ICML-2019-WangZB #bias #matter: Bias Also Matters: Bias Attribution for Deep Neural Network Explanation (SW, TZ, JAB), pp. 6659–6667.
ICML-2019-WangZB19a: Jumpout : Improved Dropout for Deep Neural Networks with ReLUs (SW, TZ, JAB), pp. 6668–6676.
ICML-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.
ICML-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.
ICML-2019-WuSZFYW #graph: Simplifying Graph Convolutional Networks (FW, AHSJ, TZ, CF, TY, KQW), pp. 6861–6871.
ICML-2019-YangWLCXS0X #named #performance: LegoNet: Efficient Convolutional Neural Networks with Lego Filters (ZY, YW, CL, HC, CX, BS, CX0, CX0), pp. 7005–7014.
ICML-2019-YoonSM #adaptation #learning #named: TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning (SWY, JS, JM), pp. 7115–7123.
ICML-2019-YouYL #graph: Position-aware Graph Neural Networks (JY, RY, JL), pp. 7134–7143.
ICML-2019-YuCGY #graph #learning #named: DAG-GNN: DAG Structure Learning with Graph Neural Networks (YY, JC, TG, MY), pp. 7154–7163.
ICML-2019-YurochkinAGGHK #learning #parametricity: Bayesian Nonparametric Federated Learning of Neural Networks (MY, MA, SG, KHG, TNH, YK), pp. 7252–7261.
ICML-2019-YuTRKSAZL #distributed #learning: Distributed Learning over Unreliable Networks (CY, HT, CR, SK, AS, DA, CZ, JL0), pp. 7202–7212.
ICML-2019-Zhang #invariant: Making Convolutional Networks Shift-Invariant Again (RZ), pp. 7324–7334.
ICML-2019-ZhangGMO #generative #self: Self-Attention Generative Adversarial Networks (HZ0, IJG, DNM, AO), pp. 7354–7363.
ICML-2019-ZhangHK #design #distributed #graph #named: Circuit-GNN: Graph Neural Networks for Distributed Circuit Design (GZ, HH, DK), pp. 7364–7373.
ICML-2019-ZhangS #learning: Co-Representation Network for Generalized Zero-Shot Learning (FZ, GS), pp. 7434–7443.
ICML-2019-ZhangZ: Interpreting Adversarially Trained Convolutional Neural Networks (TZ, ZZ), pp. 7502–7511.
ICML-2019-ZhaoHDSZ #using: Improving Neural Network Quantization without Retraining using Outlier Channel Splitting (RZ, YH, JD, CDS, ZZ), pp. 7543–7552.
ICML-2019-ZhouLLLZZ #comprehension #towards: Toward Understanding the Importance of Noise in Training Neural Networks (MZ, TL, YL, DL, EZ, TZ), pp. 7594–7602.
KDD-2019-0001WAT #graph: Graph Convolutional Networks with EigenPooling (YM0, SW, CCA, JT), pp. 723–731.
KDD-2019-AmelkinS #recommendation #social: Fighting Opinion Control in Social Networks via Link Recommendation (VA, AKS), pp. 677–685.
KDD-2019-CenZZYZ0 #learning #multi #representation: Representation Learning for Attributed Multiplex Heterogeneous Network (YC, XZ, JZ, HY, JZ, JT0), pp. 1358–1368.
KDD-2019-ChenBF #modelling #on the: On Dynamic Network Models and Application to Causal Impact (YCC, ASB, JLF), pp. 1194–1204.
KDD-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.
KDD-2019-Do0V #graph transformation #policy #predict: Graph Transformation Policy Network for Chemical Reaction Prediction (KD, TT0, SV), pp. 750–760.
KDD-2019-DongBB: Network Density of States (KD, ARB, DB), pp. 1152–1161.
KDD-2019-Eliassi-RadCL #bias: Incompleteness in Networks: Biases, Skewed Results, and Some Solutions (TER, RSC, TL), pp. 3217–3218.
KDD-2019-FanZHSHML #graph #recommendation: Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation (SF, JZ, XH, CS, LH, BM, YL), pp. 2478–2486.
KDD-2019-FawazKPSM #quantum: Training and Meta-Training Binary Neural Networks with Quantum Computing (AF, PK, SP, SS, PM), pp. 1674–1681.
KDD-2019-GaoJ #graph #learning #representation: Graph Representation Learning via Hard and Channel-Wise Attention Networks (HG, SJ), pp. 741–749.
KDD-2019-GaoPH #graph #random: Conditional Random Field Enhanced Graph Convolutional Neural Networks (HG, JP, HH), pp. 276–284.
KDD-2019-GaoPH19a #generative #named #proximity: ProGAN: Network Embedding via Proximity Generative Adversarial Network (HG, JP, HH), pp. 1308–1316.
KDD-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.
KDD-2019-GrislainPT #probability #realtime: Recurrent Neural Networks for Stochastic Control in Real-Time Bidding (NG, NP0, AT), pp. 2801–2809.
KDD-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.
KDD-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.
KDD-2019-HartvigsenSKR #adaptation #classification #policy: Adaptive-Halting Policy Network for Early Classification (TH, CS, XK, EAR), pp. 101–110.
KDD-2019-HeLLH #learning: Learning Network-to-Network Model for Content-rich Network Embedding (ZH, JL0, NL, YH), pp. 1037–1045.
KDD-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.
KDD-2019-HuangSLH #graph #random: Graph Recurrent Networks With Attributed Random Walks (XH, QS, YL, XH), pp. 732–740.
KDD-2019-HuFS #learning: Adversarial Learning on Heterogeneous Information Networks (BH, YF0, CS), pp. 120–129.
KDD-2019-HuH #learning #named #set: Sets2Sets: Learning from Sequential Sets with Neural Networks (HH, XH0), pp. 1491–1499.
KDD-2019-JinHSWLSK #email: Smart Roles: Inferring Professional Roles in Email Networks (DJ, MH, TS, MW, WL, LS, DK), pp. 2923–2933.
KDD-2019-JinRKKRK #summary: Latent Network Summarization: Bridging Network Embedding and Summarization (DJ, RAR, EK, SK, AR, DK), pp. 987–997.
KDD-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.
KDD-2019-KumarZL #interactive #predict: Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks (SK, XZ, JL), pp. 1269–1278.
KDD-2019-LiGL0 #adaptation #feature model: Adaptive Unsupervised Feature Selection on Attributed Networks (JL, RG, CL, HL0), pp. 92–100.
KDD-2019-LiQWM #ranking: Combining Decision Trees and Neural Networks for Learning-to-Rank in Personal Search (PL0, ZQ, XW, DM), pp. 2032–2040.
KDD-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.
KDD-2019-LiuTLYZH: Is a Single Vector Enough?: Exploring Node Polysemy for Network Embedding (NL, QT, YL, HY, JZ, XH), pp. 932–940.
KDD-2019-MaKL #recommendation: Hierarchical Gating Networks for Sequential Recommendation (CM, PK, XL), pp. 825–833.
KDD-2019-MartinM #quality #statistics: Statistical Mechanics Methods for Discovering Knowledge from Modern Production Quality Neural Networks (CHM, MWM), pp. 3239–3240.
KDD-2019-MengZXZX #predict: A Hierarchical Career-Path-Aware Neural Network for Job Mobility Prediction (QM, HZ, KX, LZ, HX), pp. 14–24.
KDD-2019-OuyangZLZXLD #predict: Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction (WO, XZ, LL, HZ, XX, ZL, YD), pp. 2078–2086.
KDD-2019-PangSH #detection: Deep Anomaly Detection with Deviation Networks (GP, CS, AvdH), pp. 353–362.
KDD-2019-ParkKDZF #graph #using: Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks (NP, AK, XLD, TZ, CF), pp. 596–606.
KDD-2019-RozenshteinG #mining: Mining Temporal Networks (PR, AG), pp. 3225–3226.
KDD-2019-ShangSL0 #mining: Constructing and Mining Heterogeneous Information Networks from Massive Text (JS, JS, LL, JH0), pp. 3191–3192.
KDD-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.
KDD-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.
KDD-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.
KDD-2019-TuM0P0 #named #optimisation: AutoNE: Hyperparameter Optimization for Massive Network Embedding (KT, JM, PC0, JP, WZ0), pp. 216–225.
KDD-2019-VermaZ #graph: Stability and Generalization of Graph Convolutional Neural Networks (SV, ZLZ), pp. 1539–1548.
KDD-2019-Wang00LC #graph #named #recommendation: KGAT: Knowledge Graph Attention Network for Recommendation (XW, XH0, YC0, ML0, TSC), pp. 950–958.
KDD-2019-WangWZPL #algorithm #personalisation #recommendation: Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation (JW, NW, WXZ, FP, XL), pp. 539–547.
KDD-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.
KDD-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.
KDD-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.
KDD-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.
KDD-2019-WuHX #classification #graph #named: DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification (JW, JH, JX), pp. 406–415.
KDD-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.
KDD-2019-XuH0D #predict #social: Link Prediction with Signed Latent Factors in Signed Social Networks (PX, WH, JW0, BD), pp. 1046–1054.
KDD-2019-Yang #framework #graph #named #platform: AliGraph: A Comprehensive Graph Neural Network Platform (HY), pp. 3165–3166.
KDD-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.
KDD-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.
KDD-2019-ZhangSHSC #graph: Heterogeneous Graph Neural Network (CZ, DS, CH0, AS, NVC), pp. 793–803.
KDD-2019-ZhaoDSZLX #learning #multi #relational: Multiple Relational Attention Network for Multi-task Learning (JZ, BD, LS, FZ, WL, HX), pp. 1123–1131.
KDD-2019-ZheSX #community #detection #scalability: Community Detection on Large Complex Attribute Network (CZ, AS, XX), pp. 2041–2049.
KDD-2019-ZhouMGBB #comprehension #using: Understanding Consumer Journey using Attention based Recurrent Neural Networks (YZ, SM, JG, TB, NB), pp. 3102–3111.
KDD-2019-ZhouMZ #memory management #personalisation #recommendation #topic: Topic-Enhanced Memory Networks for Personalised Point-of-Interest Recommendation (XZ, CM, ZZ), pp. 3018–3028.
KDD-2019-ZhuZ00 #graph #robust: Robust Graph Convolutional Networks Against Adversarial Attacks (DZ, ZZ, PC0, WZ0), pp. 1399–1407.
KDD-2019-ZugnerG #graph #robust: Certifiable Robustness and Robust Training for Graph Convolutional Networks (DZ, SG), pp. 246–256.
MoDELS-2019-BurguenoCG #architecture #model transformation: An LSTM-Based Neural Network Architecture for Model Transformations (LB, JC, SG), pp. 294–299.
OOPSLA-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.
PLATEAU-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.
PEPM-2019-XuZWCGX: Method name suggestion with hierarchical attention networks (SX, SZ, WW, XC, CG, JX0), pp. 10–21.
PLDI-2019-AndersonPDC #abstraction #approach #optimisation #robust: Optimization and abstraction: a synergistic approach for analyzing neural network robustness (GA, SP, ID, SC), pp. 731–744.
PLDI-2019-SmolkaKKFHK0 #probability #scalability #verification: Scalable verification of probabilistic networks (SS, PK0, DMK, NF, JH, DK, AS0), pp. 190–203.
POPL-2019-SinghGPV #abstract domain: An abstract domain for certifying neural networks (GS, TG, MP, MTV), p. 30.
SAS-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.
ASE-2019-BaoLWF #automation #generative #named: ACTGAN: Automatic Configuration Tuning for Software Systems with Generative Adversarial Networks (LB, XL, FW, BF), pp. 465–476.
ASE-2019-BuiYJ #named: AutoFocus: Interpreting Attention-Based Neural Networks by Code Perturbation (NDQB, YY, LJ), pp. 38–41.
ASE-2019-ChenPSAZ #cyber-physical #fuzzing #testing: Learning-Guided Network Fuzzing for Testing Cyber-Physical System Defences (YC, CMP, JS, SA, FZ), pp. 962–973.
ASE-2019-DuX000Z #analysis #framework: A Quantitative Analysis Framework for Recurrent Neural Network (XD, XX, YL0, LM0, YL0, JZ), pp. 1062–1065.
ASE-2019-GopinathCPT: Property Inference for Deep Neural Networks (DG, HC, CSP, AT), pp. 797–809.
ASE-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.
ASE-2019-XieCLM0Z #fuzzing: Coverage-Guided Fuzzing for Feedforward Neural Networks (XX, HC, YL0, LM0, YL0, JZ), pp. 1162–1165.
ESEC-FSE-2019-Golzadeh #congruence #dependence: Analysing socio-technical congruence in the package dependency network of Cargo (MG), pp. 1226–1228.
ESEC-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.
ASPLOS-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.
ASPLOS-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.
ASPLOS-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.
ASPLOS-2019-RouhaniCK #framework #named: DeepSigns: An End-to-End Watermarking Framework for Ownership Protection of Deep Neural Networks (BDR, HC, FK), pp. 485–497.
CASE-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.
CASE-2019-JiangL #bound #game studies: Bayesian Stackelberg Game Model for Water Supply Networks against Bounded Rational Interdictors (JJ, XL), pp. 842–847.
CASE-2019-KebriaKJN19a #fuzzy #nondeterminism: Type-2 Fuzzy Neural Network Synchronization of Teleoperation Systems with Delay and Uncertainties (PMK, AK, SMJJ, SN), pp. 1625–1630.
CASE-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.
CASE-2019-MaoDSK #optimisation: Optimizing a UAV-based Emergency Medical Service Network for Trauma Injury Patients (RM, BD, DS, NK), pp. 721–726.
CASE-2019-Martinez-SeisLW #community #quality #social: Measure community quality by attribute importance and density in social networks (BMS, XL, XW), pp. 628–633.
CASE-2019-NguyenLLGL #self #using: Visual-Guided Robot Arm Using Self-Supervised Deep Convolutional Neural Networks (VTN, CL, CHGL, SMG, JJJL), pp. 1415–1420.
CASE-2019-NiuLNLW #detection #fault #generative #named #using: DefectGAN: Weakly-Supervised Defect Detection using Generative Adversarial Network (SN, HL, TN, BL, XW), pp. 127–132.
CASE-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.
CASE-2019-SubramanianC #automation #embedded #normalisation: Mean Spectral Normalization of Deep Neural Networks for Embedded Automation (AKS, NYC), pp. 249–256.
CASE-2019-SunLY #detection #using: Railway Joint Detection Using Deep Convolutional Neural Networks (YS, YL, CY), pp. 235–240.
CASE-2019-SunW #algorithm #assembly #design #hybrid: Hybrid Evolutionary Algorithm for Integrated Supply Chain Network Design With Assembly Line Balancing (BqS, LW), pp. 885–890.
CASE-2019-TsayL #automation #multi #visual notation: Automating Visual Inspection of Lyophilized Drug Products With Multi-Input Deep Neural Networks (CT, ZL), pp. 1802–1807.
CASE-2019-WuPZ #algorithm #fault tolerance #heuristic: A heuristic pathfinding algorithm for dynamic fault tolerance in manufacturing networks (YW, GP, HZ), pp. 1580–1585.
CASE-2019-ZhangDWLL #estimation: Remaining Useful Life Estimation Based on a New Convolutional and Recurrent Neural Network (XZ, YD, LW, FL, WL), pp. 317–322.
CASE-2019-ZhangLWGG #fault #learning #using: Fault Diagnosis Using Unsupervised Transfer Learning Based on Adversarial Network (ZZ, XL, LW, LG0, YG), pp. 305–310.
CASE-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.
CASE-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.
FASE-2019-EniserGS #fault #locality #named: DeepFault: Fault Localization for Deep Neural Networks (HFE, SG, AS0), pp. 171–191.
CAV-2019-CeskaK #abstraction #analysis: Semi-quantitative Abstraction and Analysis of Chemical Reaction Networks (MC0, JK), pp. 475–496.
CAV-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.
CAV-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.
CAV-2019-GiannarakisBMW #fault tolerance #performance #refinement #verification: Efficient Verification of Network Fault Tolerance via Counterexample-Guided Refinement (NG, RB, RM, DW), pp. 305–323.
ICST-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.
ICSA-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.
JCDL-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.
JCDL-2018-SiegelLPA: Extracting Scientific Figures with Distantly Supervised Neural Networks (NS, NL, RP, WA), pp. 223–232.
JCDL-2018-ZhangW #ranking: Ranking Scientific Papers and Venues in Heterogeneous Academic Networks by Mutual Reinforcement (FZ, SW), pp. 127–130.
EDM-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).
ICPC-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.
ICSME-2018-DecanMC #dependence #evolution #on the: On the Evolution of Technical Lag in the npm Package Dependency Network (AD, TM, EC), pp. 404–414.
ICSME-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.
MSR-2018-DecanMC #dependence #on the #security: On the impact of security vulnerabilities in the npm package dependency network (AD, TM, EC), pp. 181–191.
SANER-2018-KatzRS #decompiler #using: Using recurrent neural networks for decompilation (DSK, JR, ES), pp. 346–356.
SANER-2018-LiSS #automation #requirements: Extracting features from requirements: Achieving accuracy and automation with neural networks (YL0, SS, GS), pp. 477–481.
SANER-2018-NguyenNPN #source code: A deep neural network language model with contexts for source code (ATN0, TDN, HDP, TNN), pp. 323–334.
CIAA-2018-Condon #algorithm #analysis #design #on the: On Design and Analysis of Chemical Reaction Network Algorithms (AC), pp. 1–3.
FM-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.
AIIDE-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-PLAY-2018-RoohiTKH #analysis: Neural Network Based Facial Expression Analysis of GameEvents: A Cautionary Tale (SR, JT, JMK, PH), pp. 429–437.
CIG-2018-WanK #evaluation: Building Evaluation Functions for Chess and Shogi with Uniformity Regularization Networks (SW, TK), pp. 1–8.
CIG-2018-WoofC #game studies #learning: Learning to Play General Video-Games via an Object Embedding Network (WW, KC), pp. 1–8.
FDG-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.
CIKM-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.
CIKM-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.
CIKM-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.
CIKM-2018-ChaeKKL #collaboration #framework #generative #named: CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks (DKC, JSK, SWK, JTL), pp. 137–146.
CIKM-2018-ChenQLS #quote: “Bridge”: Enhanced Signed Directed Network Embedding (YC, TQ, HL, KS), pp. 773–782.
CIKM-2018-ChenSTPCS: Enhanced Network Embeddings via Exploiting Edge Labels (HC, XS, YT, BP, MC, SS), pp. 1579–1582.
CIKM-2018-ChenWH #graph #predict: Incorporating Corporation Relationship via Graph Convolutional Neural Networks for Stock Price Prediction (YC, ZW, XH), pp. 1655–1658.
CIKM-2018-CirsteaMMG0 #correlation #multi #using: Correlated Time Series Forecasting using Multi-Task Deep Neural Networks (RGC, DVM, GMM, CG, BY0), pp. 1527–1530.
CIKM-2018-DerrAT #modelling: Signed Network Modeling Based on Structural Balance Theory (TD, CCA, JT), pp. 557–566.
CIKM-2018-EsuliF0 #quantifier #sentiment: A Recurrent Neural Network for Sentiment Quantification (AE, AMF, FS0), pp. 1775–1778.
CIKM-2018-FanLFSL #identification #semantics: A Globalization-Semantic Matching Neural Network for Paraphrase Identification (MF, WL, YF, MS, PL0), pp. 2067–2075.
CIKM-2018-FanSW #detection: Abnormal Event Detection via Heterogeneous Information Network Embedding (SF, CS, XW0), pp. 1483–1486.
CIKM-2018-GalimbertiBBCG #mining: Mining (maximal) Span-cores from Temporal Networks (EG, AB, FB, CC, FG), pp. 107–116.
CIKM-2018-GaoJLD0 #identification #social: User Identification with Spatio-Temporal Awareness across Social Networks (XG, WJ, YL0, YD, WD0), pp. 1831–1834.
CIKM-2018-GuanJWC #graph: Shared Embedding Based Neural Networks for Knowledge Graph Completion (SG, XJ, YW, XC), pp. 247–256.
CIKM-2018-GuoCZGL #detection #social: Rumor Detection with Hierarchical Social Attention Network (HG, JC, YZ, JG, JL), pp. 943–951.
CIKM-2018-HidasiK #recommendation: Recurrent Neural Networks with Top-k Gains for Session-based Recommendations (BH, AK), pp. 843–852.
CIKM-2018-HosseiniCWSS #named: HeteroMed: Heterogeneous Information Network for Medical Diagnosis (AH, TC0, WW, YS, MS), pp. 763–772.
CIKM-2018-HuangZZC #named #predict: DeepCrime: Attentive Hierarchical Recurrent Networks for Crime Prediction (CH0, JZ, YZ, NVC), pp. 1423–1432.
CIKM-2018-HuSZY #recommendation: Local and Global Information Fusion for Top-N Recommendation in Heterogeneous Information Network (BH, CS, WXZ, TY), pp. 1683–1686.
CIKM-2018-HuZY #collaboration #named #recommendation: CoNet: Collaborative Cross Networks for Cross-Domain Recommendation (GH, YZ, QY), pp. 667–676.
CIKM-2018-JiangW #named #query: RIN: Reformulation Inference Network for Context-Aware Query Suggestion (JYJ, WW0), pp. 197–206.
CIKM-2018-KangFYXCT #multi #named #ranking: X-Rank: Explainable Ranking in Complex Multi-Layered Networks (JK, SF, HY, YX, NC, HT), pp. 1959–1962.
CIKM-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.
CIKM-2018-LiGC #named: VTeller: Telling the Values Somewhere, Sometime in a Dynamic Network of Urban Systems (YL, TG, CXC), pp. 577–586.
CIKM-2018-Liu0SZ #predict: Hierarchical Complementary Attention Network for Predicting Stock Price Movements with News (QL, XC0, SS, SZ), pp. 1603–1606.
CIKM-2018-LiuCYZLS #detection #graph: Heterogeneous Graph Neural Networks for Malicious Account Detection (ZL, CC, XY, JZ, XL, LS), pp. 2077–2085.
CIKM-2018-LvZCXYL #predict: Homepage Augmentation by Predicting Links in Heterogenous Networks (JL, JZ, WC, QX, ZY, QL0), pp. 1611–1614.
CIKM-2018-MeiRCNMN #interactive #recommendation: An Attentive Interaction Network for Context-aware Recommendations (LM, PR, ZC, LN, JM0, JYN), pp. 157–166.
CIKM-2018-PalC: Label Propagation with Neural Networks (AP, DC), pp. 1671–1674.
CIKM-2018-SarkarB0 #on the: On Rich Clubs of Path-Based Centralities in Networks (SS, SB, AM0), pp. 567–576.
CIKM-2018-ShuaiLYLLY #behaviour #social: Newsfeed Filtering and Dissemination for Behavioral Therapy on Social Network Addictions (HHS, YCL, DNY, YFL, WCL, PSY), pp. 597–606.
CIKM-2018-SunJSPOW #multi #pointer #summary: Multi-Source Pointer Network for Product Title Summarization (FS, PJ, HS, CP, WO, XW), pp. 7–16.
CIKM-2018-Tang #probability #social: Stochastic Coupon Probing in Social Networks (ST), pp. 1023–1031.
CIKM-2018-Yu0LYL #adaptation #identification #recommendation #social: Adaptive Implicit Friends Identification over Heterogeneous Network for Social Recommendation (JY, MG0, JL, HY, HL0), pp. 357–366.
CIKM-2018-ZhangLNLX #graph #multi: Multiresolution Graph Attention Networks for Relevance Matching (TZ, BL, DN, KL, YX), pp. 933–942.
CIKM-2018-ZhangLS: Improve Network Embeddings with Regularization (YZ0, JL, OS), pp. 1643–1646.
CIKM-2018-ZhouYWBEYZW #named #personalisation #ranking: PRRE: Personalized Relation Ranking Embedding for Attributed Networks (SZ, HY, XW0, JB, ME, PY, JZ, CW0), pp. 823–832.
ECIR-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.
ECIR-2018-GalkoE #retrieval: Biomedical Question Answering via Weighted Neural Network Passage Retrieval (FG, CE), pp. 523–528.
ECIR-2018-RepkeK #email: Bringing Back Structure to Free Text Email Conversations with Recurrent Neural Networks (TR, RK), pp. 114–126.
ICML-2018-AroraCH #on the #optimisation: On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization (SA, NC, EH), pp. 244–253.
ICML-2018-Baity-JesiSGSAC: Comparing Dynamics: Deep Neural Networks versus Glassy Systems (MBJ, LS, MG, SS, GBA, CC, YL, MW, GB), pp. 324–333.
ICML-2018-BalestrieroB: A Spline Theory of Deep Networks (RB, RGB), pp. 383–392.
ICML-2018-BangS #generative #using: Improved Training of Generative Adversarial Networks using Representative Features (DB, HS), pp. 442–451.
ICML-2018-BinkowskiMD: Autoregressive Convolutional Neural Networks for Asynchronous Time Series (MB, GM, PD), pp. 579–588.
ICML-2018-BojanowskiJLS #generative #optimisation: Optimizing the Latent Space of Generative Networks (PB, AJ, DLP, AS), pp. 599–608.
ICML-2018-CaiYZHY #architecture #performance: Path-Level Network Transformation for Efficient Architecture Search (HC, JY, WZ0, SH, YY0), pp. 677–686.
ICML-2018-ChenBLR #adaptation #multi #named #normalisation: GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks (ZC0, VB, CYL, AR), pp. 793–802.
ICML-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.
ICML-2018-ChenZS #graph #probability #reduction: Stochastic Training of Graph Convolutional Networks with Variance Reduction (JC, JZ0, LS), pp. 941–949.
ICML-2018-DabneyOSM #learning: Implicit Quantile Networks for Distributional Reinforcement Learning (WD, GO, DS, RM), pp. 1104–1113.
ICML-2018-DaiZGW #using: Compressing Neural Networks using the Variational Information Bottleneck (BD, CZ, BG, DPW), pp. 1143–1152.
ICML-2018-DezfouliBN: Variational Network Inference: Strong and Stable with Concrete Support (AD, EVB, RN), pp. 1212–1221.
ICML-2018-DiengRAB #named: Noisin: Unbiased Regularization for Recurrent Neural Networks (ABD, RR, JA, DMB), pp. 1251–1260.
ICML-2018-DraxlerVSH #energy: Essentially No Barriers in Neural Network Energy Landscape (FD, KV, MS, FAH), pp. 1308–1317.
ICML-2018-DuL #on the #polynomial #power of: On the Power of Over-parametrization in Neural Networks with Quadratic Activation (SSD, JDL), pp. 1328–1337.
ICML-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.
ICML-2018-FurlanelloLTIA: Born-Again Neural Networks (TF, ZCL, MT, LI, AA), pp. 1602–1611.
ICML-2018-GaoCL #named #optimisation: Spotlight: Optimizing Device Placement for Training Deep Neural Networks (YG, LC0, BL), pp. 1662–1670.
ICML-2018-GaoW #learning #parallel: Parallel Bayesian Network Structure Learning (TG, DW), pp. 1671–1680.
ICML-2018-GhoshYD #learning: Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors (SG, JY, FDV), pp. 1739–1748.
ICML-2018-GilraG #learning: Non-Linear Motor Control by Local Learning in Spiking Neural Networks (AG, WG), pp. 1768–1777.
ICML-2018-HefnyM0SG #policy #predict: Recurrent Predictive State Policy Networks (AH, ZM, WS0, SSS, GJG), pp. 1954–1963.
ICML-2018-HelfrichWY #orthogonal: Orthogonal Recurrent Neural Networks with Scaled Cayley Transform (KH, DW, QY0), pp. 1974–1983.
ICML-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.
ICML-2018-JiaLQA: Exploring Hidden Dimensions in Parallelizing Convolutional Neural Networks (ZJ, SL, CRQ, AA), pp. 2279–2288.
ICML-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.
ICML-2018-JinKL #testing: Network Global Testing by Counting Graphlets (JJ, ZTK, SL), pp. 2338–2346.
ICML-2018-JinYXYJFY #named #performance: WSNet: Compact and Efficient Networks Through Weight Sampling (XJ, YY, NX0, JY, NJ, JF, SY), pp. 2357–2366.
ICML-2018-KhrulkovO #generative #geometry: Geometry Score: A Method For Comparing Generative Adversarial Networks (VK, IVO), pp. 2626–2634.
ICML-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.
ICML-2018-KumarSJ #kernel #metric: Trainable Calibration Measures For Neural Networks From Kernel Mean Embeddings (AK, SS, UJ), pp. 2810–2819.
ICML-2018-LakeB #composition: Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks (BML, MB), pp. 2879–2888.
ICML-2018-LaurentB #linear: Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global (TL0, JvB), pp. 2908–2913.
ICML-2018-LaurentB18a #multi: The Multilinear Structure of ReLU Networks (TL0, JvB), pp. 2914–2922.
ICML-2018-LeePCXS: Gated Path Planning Networks (LL, EP, DSC, EPX, RS), pp. 2953–2961.
ICML-2018-LiangSLS #classification #comprehension: Understanding the Loss Surface of Neural Networks for Binary Classification (SL, RS, YL, RS), pp. 2840–2849.
ICML-2018-LiGD #bias #induction #learning: Explicit Inductive Bias for Transfer Learning with Convolutional Networks (XL0, YG, FD), pp. 2830–2839.
ICML-2018-LiH #approach #learning: An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks (QL, SH), pp. 2991–3000.
ICML-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.
ICML-2018-MartinLV #approximate #clustering #performance: Fast Approximate Spectral Clustering for Dynamic Networks (LM, AL, PV), pp. 3420–3429.
ICML-2018-MehrabiTY #approximate #bound #power of: Bounds on the Approximation Power of Feedforward Neural Networks (MM, AT, MIY), pp. 3450–3458.
ICML-2018-MetzlerSVB #flexibility #named #retrieval #robust: prDeep: Robust Phase Retrieval with a Flexible Deep Network (CAM, PS, AV, RGB), pp. 3498–3507.
ICML-2018-MiconiSC: Differentiable plasticity: training plastic neural networks with backpropagation (TM, KOS, JC), pp. 3556–3565.
ICML-2018-MirmanGV #abstract interpretation #robust: Differentiable Abstract Interpretation for Provably Robust Neural Networks (MM, TG, MTV), pp. 3575–3583.
ICML-2018-NguyenM0: Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions (QN0, MCM, MH0), pp. 3737–3746.
ICML-2018-NitandaS #functional: Functional Gradient Boosting based on Residual Network Perception (AN, TS), pp. 3816–3825.
ICML-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.
ICML-2018-OlivaDZPSXS: Transformation Autoregressive Networks (JBO, AD, MZ, BP, RS, EPX, JS), pp. 3895–3904.
ICML-2018-OstrovskiDM #generative #modelling: Autoregressive Quantile Networks for Generative Modeling (GO, WD, RM), pp. 3933–3942.
ICML-2018-Oymak #learning: Learning Compact Neural Networks with Regularization (SO), pp. 3963–3972.
ICML-2018-PangDZ #analysis #linear: Max-Mahalanobis Linear Discriminant Analysis Networks (TP, CD, JZ0), pp. 4013–4022.
ICML-2018-QiaoZ0WY #image #recognition #scalability: Gradually Updated Neural Networks for Large-Scale Image Recognition (SQ, ZZ, WS0, BW0, ALY), pp. 4185–4194.
ICML-2018-QiuCCS #named: DCFNet: Deep Neural Network with Decomposed Convolutional Filters (QQ, XC, ARC, GS), pp. 4195–4204.
ICML-2018-ReagenGAMRWB #encoding #named: Weightless: Lossy weight encoding for deep neural network compression (BR, UG, BA, MM, AMR, GYW, DB0), pp. 4321–4330.
ICML-2018-SafranS: Spurious Local Minima are Common in Two-Layer ReLU Neural Networks (IS, OS), pp. 4430–4438.
ICML-2018-SajjadiPMS: Tempered Adversarial Networks (MSMS, GP, AM, BS), pp. 4448–4456.
ICML-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.
ICML-2018-SantoroHBML #reasoning: Measuring abstract reasoning in neural networks (AS, FH, DGTB, ASM, TPL), pp. 4477–4486.
ICML-2018-SerraTR #bound #linear: Bounding and Counting Linear Regions of Deep Neural Networks (TS, CT, SR), pp. 4565–4573.
ICML-2018-SewardUBJH #first-order #generative: First Order Generative Adversarial Networks (CS, TU, UB, NJ, SH), pp. 4574–4583.
ICML-2018-SrinivasJALF #learning: Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control (AS, AJ, PA, SL, CF), pp. 4739–4748.
ICML-2018-SunYDB #matrix: Convolutional Imputation of Matrix Networks (QS, MY, DLD, SPB), pp. 4825–4834.
ICML-2018-TaoCHFC #generative: Chi-square Generative Adversarial Network (CT, LC, RH, JF, LC), pp. 4894–4903.
ICML-2018-TeyeAS #estimation #nondeterminism #normalisation: Bayesian Uncertainty Estimation for Batch Normalized Deep Networks (MT, HA, KS0), pp. 4914–4923.
ICML-2018-WangVLGGZ: Adversarial Distillation of Bayesian Neural Network Posteriors (KCW, PV, JL, LG, RBG, RSZ), pp. 5177–5186.
ICML-2018-WehrmannCB #classification #multi: Hierarchical Multi-Label Classification Networks (JW, RC, RCB), pp. 5225–5234.
ICML-2018-WeinshallCA #education #learning: Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks (DW, GC, DA), pp. 5235–5243.
ICML-2018-WeissGY #automaton #query #using: Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples (GW, YG, EY), pp. 5244–5253.
ICML-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.
ICML-2018-WenHSZCL #predict #recognition: Deep Predictive Coding Network for Object Recognition (HW, KH, JS, YZ, EC, ZL), pp. 5263–5272.
ICML-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.
ICML-2018-XuLTSKJ #graph #learning #representation: Representation Learning on Graphs with Jumping Knowledge Networks (KX, CL, YT, TS, KiK, SJ), pp. 5449–5458.
ICML-2018-YangK #modelling #process #relational: Dependent Relational Gamma Process Models for Longitudinal Networks (SY, HK), pp. 5547–5556.
ICML-2018-YeS #approach: Variable Selection via Penalized Neural Network: a Drop-Out-One Loss Approach (MY, YS), pp. 5616–5625.
ICML-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.
ICML-2018-ZhangLD #performance: Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization (JZ, QL, ISD), pp. 5801–5809.
ICML-2018-ZhangNL #geometry: Tropical Geometry of Deep Neural Networks (LZ, GN, LHL), pp. 5819–5827.
ICPR-2018-0001D #recognition: Deep Emotion Transfer Network for Cross-database Facial Expression Recognition (SL0, WD), pp. 3092–3099.
ICPR-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.
ICPR-2018-Aldana-LopezCZG #approach #learning: Dynamic Learning Rate for Neural Networks: A Fixed-Time Stability Approach (RAL, LECM, JZ, DGG, AC), pp. 1378–1383.
ICPR-2018-BallottaBVC #detection #image: Fully Convolutional Network for Head Detection with Depth Images (DB, GB, RV, RC), pp. 752–757.
ICPR-2018-BandyopadhyayNM #axiom #social: A Generic Axiomatic Characterization for Measuring Influence in Social Networks (SB, RN, MNM), pp. 2606–2611.
ICPR-2018-BhuniaBBKB0P #using: Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network (AKB, AB, AKB, AK, PB, PPR0, UP0), pp. 3639–3644.
ICPR-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.
ICPR-2018-BiciciKA: Conditional Information Gain Networks (UCB, CK, LA), pp. 1390–1395.
ICPR-2018-CaiLWY #estimation #multi: Joint Head Pose Estimation with Multi-task Cascaded Convolutional Networks for Face Alignment (ZC, QL, SW, BY), pp. 495–500.
ICPR-2018-CaoFM #multi #recognition: An End-to-End Neural Network for Multi-line License Plate Recognition (YC, HF, HM), pp. 3698–3703.
ICPR-2018-CaoLZSLS #detection #named #performance: ThinNet: An Efficient Convolutional Neural Network for Object Detection (SC, YL, CZ, QSS, PL, SS), pp. 836–841.
ICPR-2018-ChangWZ #linear #performance #self: Piecewise Linear Units for Fast Self-Normalizing Neural Networks (YC, XW, SZ), pp. 429–434.
ICPR-2018-ChengLYT #image #named #recursion: SESR: Single Image Super Resolution with Recursive Squeeze and Excitation Networks (XC, XL0, JY0, YT), pp. 147–152.
ICPR-2018-ChenYY #classification #image: Semi-supervised convolutional neural networks with label propagation for image classification (LC, SY, MY0), pp. 1319–1324.
ICPR-2018-CheQ #estimation #segmentation: Dynamic Projected Segmentation Networks For Hand Pose Estimation (YC, YQ), pp. 477–482.
ICPR-2018-ChoiCT #authentication #mobile #probability #random: One-class Random Maxout Probabilistic Network for Mobile Touchstroke Authentication (SC, IC, ABJT), pp. 3359–3364.
ICPR-2018-ChooSJC #classification #multi: Multi-scale Recurrent Encoder-Decoder Network for Dense Temporal Classification (SKC, WS, DjJ, NIC), pp. 103–108.
ICPR-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.
ICPR-2018-CuiB00JH #graph #hybrid #kernel #learning: A Deep Hybrid Graph Kernel Through Deep Learning Networks (LC, LB0, LR0, YW0, YJ0, ERH), pp. 1030–1035.
ICPR-2018-CuiLZLXGJZ #classification: Classification Guided Deep Convolutional Network for Compressed Sensing (WC, SL, SZ, YL0, HX, XG, FJ0, DZ), pp. 2905–2910.
ICPR-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.
ICPR-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.
ICPR-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.
ICPR-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.
ICPR-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.
ICPR-2018-DongZL #recognition: Dynamic Facial Expression Recognition Based on Convolutional Neural Networks with Dense Connections (JD, HZ, LL), pp. 3433–3438.
ICPR-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.
ICPR-2018-Du0CHW #adaptation #visual notation: Object-Adaptive LSTM Network for Visual Tracking (YD, YY0, SC, YH, HW), pp. 1719–1724.
ICPR-2018-DuSZ #contest #identification #multi: Which Part is Better: Multi-Part Competition Network for person Re-Identification (PD, YS, YZ0), pp. 1634–1639.
ICPR-2018-EleziTVP #learning: Transductive Label Augmentation for Improved Deep Network Learning (IE, AT, SV, MP), pp. 1432–1437.
ICPR-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.
ICPR-2018-Fan: Deep Epitome for Unravelling Generalized Hamming Network (LF), pp. 409–416.
ICPR-2018-FangYZZ: Diversified Dual Domain-Adversarial Neural Networks (YF, QY, WZ, ZZ), pp. 615–620.
ICPR-2018-GaoCZLL #3d: Background Subtraction via 3D Convolutional Neural Networks (YG, HC, XZ0, LL, ZL), pp. 1271–1276.
ICPR-2018-GarrettR #3d #performance #using: Fast Descriptor Extraction for Contextless 3D Registration Using a Fully Convolutional Network (TG, RR), pp. 1211–1216.
ICPR-2018-GasparettoRBPCB: Cross-Dataset Data Augmentation for Convolutional Neural Networks Training (AG, DR, FB, MP, LC, MB, EU, AA), pp. 910–915.
ICPR-2018-GrelssonF #exponential #learning #linear: Improved Learning in Convolutional Neural Networks with Shifted Exponential Linear Units (ShELUs) (BG, MF), pp. 517–522.
ICPR-2018-HanXW #generative #learning #multi #representation: Learning Multi-view Generator Network for Shared Representation (TH0, XX, YNW), pp. 2062–2068.
ICPR-2018-HanXZL #composition #image #learning: Learning Intrinsic Image Decomposition by Deep Neural Network with Perceptual Loss (GH, XX, WSZ, JL), pp. 91–96.
ICPR-2018-HanZG #classification #composition #multi: Multi-Frequency Decomposition with Fully Convolutional Neural Network for Time Series Classification (YH, SZ, ZG), pp. 284–289.
ICPR-2018-HeGG #learning: Structure Learning of Bayesian Networks by Finding the Optimal Ordering (CCH, XGG, ZgG), pp. 177–182.
ICPR-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.
ICPR-2018-HuiWG #image: Two-Stage Convolutional Network for Image Super-Resolution (ZH, XW, XG), pp. 2670–2675.
ICPR-2018-JeongJLCY0 #estimation: Selective Ensemble Network for Accurate Crowd Density Estimation (JJ, HJ, JL, JC, SY, JYC0), pp. 320–325.
ICPR-2018-JiangWYSLGFZ #recursion: Recursive Inception Network for Super-Resolution (TJ, XW, ZY, WS, GL, SG, HF, QZ), pp. 2759–2764.
ICPR-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.
ICPR-2018-JiuSQ #image: Deep Context Networks for Image Annotation (MJ, HS, LQ), pp. 2422–2427.
ICPR-2018-JollyIKU #design #how #question: How do Convolutional Neural Networks Learn Design? (SJ, BKI, RK, SU), pp. 1085–1090.
ICPR-2018-JyotiD #automation #estimation #geometry #using: Automatic Eye Gaze Estimation using Geometric & Texture-based Networks (SJ, AD), pp. 2474–2479.
ICPR-2018-KasemHJ #image #towards: Revised Spatial Transformer Network towards Improved Image Super-resolutions (HMK, KWH, JJ), pp. 2688–2692.
ICPR-2018-KhalidY #multi #recognition: Multi-Modal Three-Stream Network for Action Recognition (MUK, JY), pp. 3210–3215.
ICPR-2018-KonwerBBBB0P #generative #using: Staff line Removal using Generative Adversarial Networks (AK, AKB, AB, AKB, PB, PPR0, UP0), pp. 1103–1108.
ICPR-2018-KungMZ #adaptation #array: Adaptive Tiling: Applying Fixed-size Systolic Arrays To Sparse Convolutional Neural Networks (HTK, BM, SQZ), pp. 1006–1011.
ICPR-2018-LengK: Confidence-Driven Network for Point-to-Set Matching (ML, IAK), pp. 3414–3420.
ICPR-2018-LiaoZWN #detection #named #recognition: Uniface: A Unified Network for Face Detection and Recognition (ZL, PZ, QW, BN), pp. 3531–3536.
ICPR-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.
ICPR-2018-LiCQWW #adaptation #learning #semantics: Cross-domain Semantic Feature Learning via Adversarial Adaptation Networks (RL, WmC0, SQ, HSW, SW), pp. 37–42.
ICPR-2018-LiCZC #detection: Mammographic mass detection based on convolution neural network (YL, HC, LZ, LC), pp. 3850–3855.
ICPR-2018-LiHLHS #consistency #generative: Global and Local Consistent Age Generative Adversarial Networks (PL, YH, QL0, RH, ZS), pp. 1073–1078.
ICPR-2018-LiHY0CHWL #segmentation: Skin Lesion Segmentation via Dense Connected Deconvolutional Network (HL, XH, ZY, FZ0, JZC, LH, TW, BL), pp. 671–675.
ICPR-2018-LingLZG #classification #image #learning: Semi-Supervised Learning via Convolutional Neural Network for Hyperspectral Image Classification (ZL, XL, WZ, SG), pp. 1–6.
ICPR-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.
ICPR-2018-LiuDJQ #classification #image #visual notation: Visual Tree Convolutional Neural Network in Image Classification (YL, YD, RJ, PQ), pp. 758–763.
ICPR-2018-LiuGCL #generative: An Extensive Study of Cycle-Consistent Generative Networks for Image-to-Image Translation (YL0, YG, WC, MSL), pp. 219–224.
ICPR-2018-LiuHH #classification: Lifting Scheme Based Deep Network Model for Remote Sensing Imagery Classification (XL, BH, CH), pp. 688–693.
ICPR-2018-LiuMHWLB: Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting (XL, MM, LH, JvdW, AML0, ADB), pp. 2262–2268.
ICPR-2018-LiuMXP #image #semantics #synthesis: Semantic Image Synthesis via Conditional Cycle-Generative Adversarial Networks (XL, GM, SX, CP), pp. 988–993.
ICPR-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.
ICPR-2018-LiW #estimation #image: Local Regression Based Hourglass Network for Hand Pose Estimation from a Single Depth Image (JL, ZW), pp. 1767–1772.
ICPR-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.
ICPR-2018-LocBO #documentation #image #security #using: Document Images Watermarking for Security Issue using Fully Convolutional Networks (CVL, JCB, JMO), pp. 1091–1096.
ICPR-2018-LuoKW #generative #image #synthesis: Traffic Sign Image Synthesis with Generative Adversarial Networks (HL, QK, FW), pp. 2540–2545.
ICPR-2018-LuoY #architecture #performance #segmentation: Fast Skin Lesion Segmentation via Fully Convolutional Network with Residual Architecture and CRF (WL, MY), pp. 1438–1443.
ICPR-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.
ICPR-2018-MaH #generative #using: Perceptual Face Completion using a Local-Global Generative Adversarial Network (RM, HH0), pp. 1670–1675.
ICPR-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.
ICPR-2018-MaLHY #estimation #hybrid #using: Image-based Air Pollution Estimation Using Hybrid Convolutional Neural Network (JM, KL, YH, JY), pp. 471–476.
ICPR-2018-ManessiRBNS #automation: Automated Pruning for Deep Neural Network Compression (FM, AR, SB, PN, RS), pp. 657–664.
ICPR-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.
ICPR-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.
ICPR-2018-MohantyDG18a #detection #robust: Robust Scene Text Detection with Deep Feature Pyramid Network and CNN based NMS Model (SM, TD, HPG), pp. 3741–3746.
ICPR-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.
ICPR-2018-OGormanW #video: Balancing Video Analytics Processing and Bandwidth for Edge-Cloud Networks (LO, XW), pp. 2618–2623.
ICPR-2018-ParkAMLCP0PK #generative #named: MMGAN: Manifold-Matching Generative Adversarial Networks (NP, AA, JRAM, KL, JC, DKP, TC0, HP, YK), pp. 1343–1348.
ICPR-2018-PassalisT #information management #similarity #using: Neural Network Knowledge Transfer using Unsupervised Similarity Matching (NP, AT), pp. 716–721.
ICPR-2018-PereraAP #generative #multi #named #using: In2I: Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks (PP, MA, VMP), pp. 140–146.
ICPR-2018-Pham0V #graph #memory management #predict #process: Graph Memory Networks for Molecular Activity Prediction (TP, TT0, SV), pp. 639–644.
ICPR-2018-PiantadosiSS #segmentation: Breast Segmentation in MRI via U-Net Deep Convolutional Neural Networks (GP, MS, CS), pp. 3917–3922.
ICPR-2018-PramerdorferKL #3d #bound #classification #multi: Multi-View Classification and 3D Bounding Box Regression Networks (CP, MK, MVL), pp. 734–739.
ICPR-2018-QiuXC #flexibility #linear #named: FReLU: Flexible Rectified Linear Units for Improving Convolutional Neural Networks (SQ, XX, BC), pp. 1223–1228.
ICPR-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.
ICPR-2018-RibaFLF #graph #learning #message passing: Learning Graph Distances with Message Passing Neural Networks (PR, AF0, JL0, AF), pp. 2239–2244.
ICPR-2018-Robles-KellyW #image: A Convolutional Neural Network for Pixelwise Illuminant Recovery in Colour and Spectral Images (ARK, RW), pp. 109–114.
ICPR-2018-ShiLLLM #recognition: Weather Recognition Based on Edge Deterioration and Convolutional Neural Networks (YS, YL, JL, XL, YLM), pp. 2438–2443.
ICPR-2018-ShiWDYL #generative: Data Augmentation with Improved Generative Adversarial Networks (HS, LW, GD, FY, XL), pp. 73–78.
ICPR-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.
ICPR-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.
ICPR-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.
ICPR-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.
ICPR-2018-SongZR #bound: UAV Target Tracking with A Boundary-Decision Network (KS, WZ0, XR), pp. 2576–2581.
ICPR-2018-SunZY #automation #embedded #generative: Pyramid Embedded Generative Adversarial Network for Automated Font Generation (DS, QZ, JY), pp. 976–981.
ICPR-2018-SwamiD #image #named: CANDY: Conditional Adversarial Networks based End-to-End System for Single Image Haze Removal (KS, SKD), pp. 3061–3067.
ICPR-2018-TongT #image #recognition: Reservoir Computing with Untrained Convolutional Neural Networks for Image Recognition (ZT, GT), pp. 1289–1294.
ICPR-2018-TsangDX #modelling: Recurrent Neural Networks for Financial Time-Series Modelling (GT, JD, XX), pp. 892–897.
ICPR-2018-Wang #named: ReNN: Rule-embedded Neural Networks (HW), pp. 824–829.
ICPR-2018-WangBZZ #generative: Generating Facial Line-drawing with Convolutional Neural Networks (YW, XB, LZ, SZ), pp. 513–516.
ICPR-2018-WangCYX #self: Self-Attention Based Network for Punctuation Restoration (FW0, WC0, ZY0, BX0), pp. 2803–2808.
ICPR-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.
ICPR-2018-WangLLL #detection #multi: Anchor Free Network for Multi-Scale Face Detection (CW, ZL, SL, SL), pp. 1554–1559.
ICPR-2018-WangXLTXX #detection #multi: Multi-scale Fusion with Context-aware Network for Object Detection (HW, JX, LL, YT, DX, SX), pp. 2486–2491.
ICPR-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.
ICPR-2018-WangZL #detection #generative: Anomaly Detection via Minimum Likelihood Generative Adversarial Networks (CW, YMZ, CLL), pp. 1121–1126.
ICPR-2018-WuLXFPL #predict: Context-Aware Attention LSTM Network for Flood Prediction (YW, ZL, WX, JF, SP, TL), pp. 1301–1306.
ICPR-2018-WuXFPL #predict: Local and Global Bayesian Network based Model for Flood Prediction (YW, WX, JF, SP, TL), pp. 225–230.
ICPR-2018-WuZ #generative: Deep Generative Adversarial Networks for the Sparse Signal Denoising (KW, CZ), pp. 1127–1132.
ICPR-2018-XiaoLLLK #algorithm: Single-image Dehazing Algorithm Based on Convolutional Neural Networks (JX, LL, EL, JL, RK), pp. 1259–1264.
ICPR-2018-XiaoW #animation #speech: Dense Convolutional Recurrent Neural Network for Generalized Speech Animation (LX, ZW), pp. 633–638.
ICPR-2018-XieHLHYL #classification: Deeply Supervised Residual Network for HEp-2 Cell Classification (HX, YH, HL, TH, ZY, BL), pp. 699–703.
ICPR-2018-XuPYL #process #recognition: Human Activity Recognition Based On Convolutional Neural Network (WX, YP, YY, YL), pp. 165–170.
ICPR-2018-XuTY #recognition: Beyond Two-stream: Skeleton-based Three-stream Networks for Action Recognition in Videos (JX, KT, HY), pp. 1567–1573.
ICPR-2018-XuW: Target Group Distribution Pattern Discovery via Convolutional Neural Network (XX, WW), pp. 266–271.
ICPR-2018-XuWJW #visual notation: Visual Tracking by Combining the Structure-Aware Network and Spatial-Temporal Regression (DX, LW, MJ, QW), pp. 1912–1917.
ICPR-2018-XuWZWZR0CH: Depth-based Subgraph Convolutional Neural Networks (CX, DW, ZZ, BW, DZ, GR, LB0, LC, ERH), pp. 1024–1029.
ICPR-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.
ICPR-2018-YamadaIYK #segmentation #using: Texture Segmentation using Siamese Network and Hierarchical Region Merging (RY, HI, NY, TK), pp. 2735–2740.
ICPR-2018-Yang0K #generative #multi: Multi-scale Generative Adversarial Networks for Crowd Counting (JY, YZ0, SYK), pp. 3244–3249.
ICPR-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.
ICPR-2018-YangY: Enhanced Network Embedding with Text Information (SY, BY), pp. 326–331.
ICPR-2018-YanJY #3d #detection #generative: 3D Convolutional Generative Adversarial Networks for Detecting Temporal Irregularities in Videos (MY, XJ, JY), pp. 2522–2527.
ICPR-2018-YanWSLZ #image #learning #using: Image Captioning using Adversarial Networks and Reinforcement Learning (SY, FW, JSS, WL, BZ), pp. 248–253.
ICPR-2018-YuLH #comparison: Curvature-based Comparison of Two Neural Networks (TY, HL, JEH), pp. 441–447.
ICPR-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.
ICPR-2018-ZhangCZ #architecture #recognition: Temporal Inception Architecture for Action Recognition with Convolutional Neural Networks (WZ, JC, HZ), pp. 3216–3221.
ICPR-2018-ZhangDKQL #detection #multi: Global Contrast Enhancement Detection via Deep Multi-Path Network (CZ, DD, LK, HQ, SL), pp. 2815–2820.
ICPR-2018-ZhangFQS #generative #image: Wasserstein Generative Recurrent Adversarial Networks for Image Generating (CZ, YF, BQ, JS), pp. 242–247.
ICPR-2018-ZhangHLZ #detection: Attention-based Neural Network for Traffic Sign Detection (JZ, LH, JL, YZ), pp. 1839–1844.
ICPR-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.
ICPR-2018-ZhangLXQ #named: LHONE: Label Homophily Oriented Network Embedding (LZ, XL, JX, YQ), pp. 665–670.
ICPR-2018-ZhangLZXSL #classification: Spatial Pyramid Dilated Network for Pulmonary Nodule Malignancy Classification (GZ, YL, DZ, YX, YS, JL), pp. 3911–3916.
ICPR-2018-ZhangSOS #identification #using: Person Re-identification Using Two-Stage Convolutional Neural Network (YZ, JS, DO, HTS), pp. 3341–3346.
ICPR-2018-ZhangSW #linear #problem: Cascade Deep Networks for Sparse Linear Inverse Problems (HZ, HS, WW), pp. 812–817.
ICPR-2018-ZhangWGWXL #detection #effectiveness #learning: An Effective Deep Learning Based Scheme for Network Intrusion Detection (HZ, CQW, SG, ZW, YX, YL), pp. 682–687.
ICPR-2018-ZhangYLQZC #detection: Single Shot Feature Aggregation Network for Underwater Object Detection (LZ, XY0, ZL, LQ, HZ, CC), pp. 1906–1911.
ICPR-2018-ZhangZDD #analysis #online #recognition: Trajectory-based Radical Analysis Network for Online Handwritten Chinese Character Recognition (JZ, YZ, JD, LD), pp. 3681–3686.
ICPR-2018-ZhangZDW #classification #image: Image Exploration Procedure Classification with Spike-timing Neural Network for the Blind (TZ, TZ, BSD, JPW), pp. 3256–3261.
ICPR-2018-ZhangZW #classification #lightweight #named: LD-CNN: A Lightweight Dilated Convolutional Neural Network for Environmental Sound Classification (XZ, YZ, WW), pp. 373–378.
ICPR-2018-ZhanLL #recognition #string #using: Handwritten Digit String Recognition using Convolutional Neural Network (HZ, SL, YL), pp. 3729–3734.
ICPR-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.
ICPR-2018-ZhaoYT #order #predict #using: Pen Tip Motion Prediction for Handwriting Drawing Order Recovery using Deep Neural Network (BZ, MY, JT), pp. 704–709.
ICPR-2018-ZhengZ #approach #classification: Accelerating the Classification of Very Deep Convolutional Network by A Cascading Approach (WZ, ZZ), pp. 355–360.
ICPR-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.
ICPR-2018-ZhongYZ: Merging Neurons for Structure Compression of Deep Networks (GZ0, HY, HZ0), pp. 1462–1467.
ICPR-2018-ZhuZ #detection: A Comprehensive Study on Upper-Body Detection with Deep Neural Networks (YZ, LZ), pp. 171–176.
ICPR-2018-ZhuZLLZC #identification: A Shortly and Densely Connected Convolutional Neural Network for Vehicle Re-identification (JZ, HZ, ZL, SL, LZ, CC), pp. 3285–3290.
KDD-2018-0022PYT #algorithm #effectiveness #optimisation: Network Connectivity Optimization: Fundamental Limits and Effective Algorithms (CC0, RP, LY, HT), pp. 1167–1176.
KDD-2018-ChenGCSSJ #3d #image: Voxel Deconvolutional Networks for 3D Brain Image Labeling (YC, HG, LC, MS, DS, SJ), pp. 1226–1234.
KDD-2018-ChenLB #social: Quantifying and Minimizing Risk of Conflict in Social Networks (XC, JL, TDB), pp. 1197–1205.
KDD-2018-ChenYWWNL #metric #named #predict: PME: Projected Metric Embedding on Heterogeneous Networks for Link Prediction (HC, HY, WW0, HW0, QVHN, XL), pp. 1177–1186.
KDD-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.
KDD-2018-ConteFGMSU #similarity: Node Similarity with q -Grams for Real-World Labeled Networks (AC, GF, RG, AM, KS, TU), pp. 1282–1291.
KDD-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.
KDD-2018-DizajiWH #generative: Semi-Supervised Generative Adversarial Network for Gene Expression Inference (KGD, XW, HH), pp. 1435–1444.
KDD-2018-GaoH #self: Self-Paced Network Embedding (HG, HH), pp. 1406–1415.
KDD-2018-GaoWJ #graph #scalability: Large-Scale Learnable Graph Convolutional Networks (HG, ZW, SJ), pp. 1416–1424.
KDD-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.
KDD-2018-HarelR #prototype #using: Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks (SH, KR), pp. 331–339.
KDD-2018-LeeGZ #generative #query: Rare Query Expansion Through Generative Adversarial Networks in Search Advertising (MCL, BG, RZ), pp. 500–508.
KDD-2018-Lian0ZGCT0 #higher-order #proximity: High-order Proximity Preserving Information Network Hashing (DL, KZ0, VWZ, YG, LC, IWT, XX0), pp. 1744–1753.
KDD-2018-LiuHLH #induction #on the #taxonomy: On Interpretation of Network Embedding via Taxonomy Induction (NL, XH, JL, XH), pp. 1812–1820.
KDD-2018-LiuHWH #self: Content to Node: Self-Translation Network Embedding (JL0, ZH, LW, YH), pp. 1794–1802.
KDD-2018-LiuKY #social: Active Opinion Maximization in Social Networks (XL, XK, PSY), pp. 1840–1849.
KDD-2018-LiY #classification #learning #policy: Learning Adversarial Networks for Semi-Supervised Text Classification via Policy Gradient (YL, JY), pp. 1715–1723.
KDD-2018-LuoCTSLCY #information management #invariant #learning #named: TINET: Learning Invariant Networks via Knowledge Transfer (CL, ZC, LAT, AS, ZL, HC, JY), pp. 1890–1899.
KDD-2018-MaCW0 #taxonomy: Hierarchical Taxonomy Aware Network Embedding (JM, PC0, XW0, WZ0), pp. 1920–1929.
KDD-2018-MolinoZW #named #ranking: COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks (PM, HZ, YCW), pp. 586–595.
KDD-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.
KDD-2018-Sanei-MehriST: Butterfly Counting in Bipartite Networks (SVSM, AES, ST), pp. 2150–2159.
KDD-2018-ShashikumarSCN #bidirectional #detection #using: Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks (SPS, AJS, GDC, SN), pp. 715–723.
KDD-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.
KDD-2018-ShiZGZ0 #learning: Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks (YS, QZ, FG, CZ0, JH0), pp. 2190–2199.
KDD-2018-SunBZWZ #modelling #multi: Subspace Network: Deep Multi-Task Censored Regression for Modeling Neurodegenerative Diseases (MS, IMB, LZ, ZW, JZ), pp. 2259–2268.
KDD-2018-TayLH #multi #recommendation: Multi-Pointer Co-Attention Networks for Recommendation (YT, ATL, SCH), pp. 2309–2318.
KDD-2018-TayTH #multi: Multi-Cast Attention Networks (YT, LAT, SCH), pp. 2299–2308.
KDD-2018-TomasiTSV: Latent Variable Time-varying Network Inference (FT, VT, SS, AV), pp. 2338–2346.
KDD-2018-TuCWY0 #equivalence #recursion: Deep Recursive Network Embedding with Regular Equivalence (KT, PC0, XW0, PSY, WZ0), pp. 2357–2366.
KDD-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.
KDD-2018-WangWLW #analysis #composition #multi: Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis (JW, ZW, JL, JW), pp. 2437–2446.
KDD-2018-WangWW: Inferring Metapopulation Propagation Network for Intra-city Epidemic Control and Prevention (JW, XW, JW), pp. 830–838.
KDD-2018-WangYHLWH #memory management #recommendation #streaming: Neural Memory Streaming Recommender Networks with Adversarial Training (QW, HY, ZH, DL, HW, ZH), pp. 2467–2475.
KDD-2018-WangZHZ #learning #recommendation: Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation (LW, WZ0, XH, HZ), pp. 2447–2456.
KDD-2018-WongPKFJ #biology #community #named #performance: SDREGION: Fast Spotting of Changing Communities in Biological Networks (SWHW, CP, MK, CF, IJ), pp. 867–875.
KDD-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.
KDD-2018-YasarC: An Iterative Global Structure-Assisted Labeled Network Aligner (AY, ÜVÇ), pp. 2614–2623.
KDD-2018-YingHCEHL #graph #recommendation: Graph Convolutional Neural Networks for Web-Scale Recommender Systems (RY, RH, KC, PE, WLH, JL), pp. 974–983.
KDD-2018-YiZWLZ #distributed #predict #quality: Deep Distributed Fusion Network for Air Quality Prediction (XY, JZ, ZW, TL, YZ0), pp. 965–973.
KDD-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.
KDD-2018-YuZCASZCW #learning: Learning Deep Network Representations with Adversarially Regularized Autoencoders (WY, CZ, WC, CCA, DS, BZ, HC, WW0), pp. 2663–2671.
KDD-2018-ZhangCWPY0 #proximity: Arbitrary-Order Proximity Preserved Network Embedding (ZZ, PC0, XW0, JP, XY, WZ0), pp. 2778–2786.
KDD-2018-ZhouHYF #named #representation #self: SPARC: Self-Paced Network Representation for Few-Shot Rare Category Characterization (DZ, JH, HY, WF), pp. 2807–2816.
KDD-2018-ZhouZSFZMYJLG #predict: Deep Interest Network for Click-Through Rate Prediction (GZ, XZ, CS, YF, HZ, XM, YY, JJ, HL, KG), pp. 1059–1068.
KDD-2018-ZhuCW0: Deep Variational Network Embedding in Wasserstein Space (DZ, PC0, DW, WZ0), pp. 2827–2836.
KDD-2018-ZugnerAG #graph: Adversarial Attacks on Neural Networks for Graph Data (DZ, AA, SG), pp. 2847–2856.
KDD-2018-ZuoLLGHW: Embedding Temporal Network via Neighborhood Formation (YZ, GL, HL, JG, XH, JW), pp. 2857–2866.
ICMT-2018-TomaszekLWS #model transformation: Virtual Network Embedding: Reducing the Search Space by Model Transformation Techniques (ST, EL, LW, AS), pp. 59–75.
Onward-2018-BasmanLC #design: The open authorial principle: supporting networks of authors in creating externalisable designs (AB, CHL, CBDC), pp. 29–43.
PLDI-2018-GehrMTVWV #named #probability: Bayonet: probabilistic inference for networks (TG, SM, PT, LV, PW, MTV), pp. 586–602.
PLDI-2018-LinWCLDW #architecture #manycore: Mapping spiking neural networks onto a manycore neuromorphic architecture (CKL, AW, GNC, THL, MD, HW), pp. 78–89.
SAS-2018-AlpernasMPSSSV #abstract interpretation: Abstract Interpretation of Stateful Networks (KA, RM, AP, MS, SS, SS, YV), pp. 86–106.
ASE-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.
ASE-2018-SunWRHKK #testing: Concolic testing for deep neural networks (YS, MW, WR, XH0, MK, DK), pp. 109–119.
ESEC-FSE-2018-Gusmanov #modelling #on the #reliability: On the adoption of neural networks in modeling software reliability (KG), pp. 962–964.
ESEC-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-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.
ASPLOS-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.
ASPLOS-2018-CaiRLDWQPW #hardware #named: VIBNN: Hardware Acceleration of Bayesian Neural Networks (RC, AR, NL0, CD, LW, XQ, MP, YW), pp. 476–488.
ASPLOS-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.
ASPLOS-2018-MarkuzeSMT #named: DAMN: Overhead-Free IOMMU Protection for Networking (AM, IS, AM0, DT), pp. 301–315.
CASE-2018-CaoWLG #fault #generative: Application of Generative Adversarial Networks for Intelligent Fault Diagnosis (SC, LW, XL, LG0), pp. 711–715.
CASE-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.
CASE-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.
CASE-2018-PetittiPCMSCN #approach #distributed #mobile: A Distributed Map Building Approach for Mobile Robotic Networks (AP, DDP, RC, AM, ES, AC, DN), pp. 116–121.
CASE-2018-SpenrathP #heuristic #random #using: Using Neural Networks for Heuristic Grasp Planning in Random Bin Picking (FS, AP), pp. 258–263.
CASE-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.
CASE-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.
ESOP-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.
FASE-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.
ICST-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.
ICTSS-2018-PrasetyaT #specification: Neural Networks as Artificial Specifications (ISWBP, MAT), pp. 135–141.
JCDL-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.
EDM-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).
EDM-2017-GuoCC #approach #student: A Neural Network Approach to Estimate Student Skill Mastery in Cognitive Diagnostic Assessments (QG, MC, YC).
EDM-2017-KuangCHN #analysis #framework #platform #social #topic: A Topic Model and Social Network Analysis of a School Blogging Platform (XK, HSC, BH, GN).
EDM-2017-MichalenkoLB #feedback #memory management #personalisation #using: Personalized Feedback for Open-Response Mathematical Questions using Long Short-Term Memory Networks (JJM, ASL, RGB).
EDM-2017-TatoND #automation #detection #reasoning: Convolutional Neural Network for Automatic Detection of Sociomoral Reasoning Level (AANT, RN, AD).
EDM-2017-YasudaNKKH #automation #using: Automatic Scoring Method for Descriptive Test Using Recurrent Neural Network (KY, IN, HK, HK, MH).
EDM-2017-ZhangS #interactive #modelling #scalability: Modeling Network Dynamics of MOOC Discussion Interactions at Scale (JZ, MS).
EDM-2017-ZhaoH #education: Estimating Individual Treatment Effect from Educational Studies with Residual Counterfactual Networks (SZ, NTH).
ICPC-2017-MostafaRW #android #behaviour #maintenance #named: NetDroid: summarizing network behavior of Android apps for network code maintenance (SM, RR, XW), pp. 165–175.
ICSME-2017-HanS #distance #evaluation #metric: Mean Average Distance to Resolver: An Evaluation Metric for Ticket Routing in Expert Network (JH, AS), pp. 594–602.
MSR-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.
SEFM-2017-WiikB #automation #data flow #specification #verification: Specification and Automated Verification of Dynamic Dataflow Networks (JW, PB), pp. 136–151.
Haskell-2017-DawsonGG #composition #monad: Composable network stacks and remote monads (JD, MG, AG), pp. 86–97.
CIG-2017-AckerLB #automaton #simulation: Cellular automata simulation on FPGA for training neural networks with virtual world imagery (OVA, OL, GB), pp. 304–305.
CIG-2017-HorsleyL #automation #generative: Building an automatic sprite generator with deep convolutional generative adversarial networks (LH, DPL), pp. 134–141.
CIG-2017-NguyenRGM #automation #learning: Automated learning of hierarchical task networks for controlling minecraft agents (CN, NR, SG, HMA), pp. 226–231.
CIG-2017-YoonK #game studies #visual notation: Deep Q networks for visual fighting game AI (SY, KJK), pp. 306–308.
FDG-2017-TengB #approach #generative #semantics: A semantic approach to patch-based procedural generation of urban road networks (ET, RB), p. 10.
ICGJ-2017-PirkerKG #analysis #game studies #social: Social network analysis of the global game jam network (JP, FK, CG), pp. 10–14.
CIKM-2017-ChavaryEL #mining #using: Summarizing Significant Changes in Network Traffic Using Contrast Pattern Mining (EAC, SME, CL), pp. 2015–2018.
CIKM-2017-ChengZZKZW #classification #sentiment: Aspect-level Sentiment Classification with HEAT (HiErarchical ATtention) Network (JC, SZ, JZ, IK, XZ0, HW0), pp. 97–106.
CIKM-2017-ChenXLDTCP #framework #named: HotSpots: Failure Cascades on Heterogeneous Critical Infrastructure Networks (LC, XX, SL, SD, AGT, SC, BAP), pp. 1599–1607.
CIKM-2017-ChenYSGHY #scalability: Community-Based Network Alignment for Large Attributed Network (ZC0, XY, BS, JG, XH, WSY), pp. 587–596.
CIKM-2017-CuiLZZ #analysis: Text Coherence Analysis Based on Deep Neural Network (BC, YL, YZ, ZZ), pp. 2027–2030.
CIKM-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.
CIKM-2017-FangYMG #scheduling: QoS-Aware Scheduling of Heterogeneous Servers for Inference in Deep Neural Networks (ZF, TY, OJM, RKG0), pp. 2067–2070.
CIKM-2017-FreitasTCX #agile #analysis: Rapid Analysis of Network Connectivity (SF, HT, NC, YX), pp. 2463–2466.
CIKM-2017-FuLL #learning #named #representation: HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning (TYF, WCL, ZL), pp. 1797–1806.
CIKM-2017-GalimbertiBG #composition #multi: Core Decomposition and Densest Subgraph in Multilayer Networks (EG, FB, FG), pp. 1807–1816.
CIKM-2017-GarciaM #energy #using: Inferring Appliance Energy Usage from Smart Meters using Fully Convolutional Encoder Decoder Networks (FCCG, EQBM), pp. 2075–2078.
CIKM-2017-Gupta0M #personalisation #ranking: Interest Diffusion in Heterogeneous Information Network for Personalized Item Ranking (MG, PK0, RM), pp. 2087–2090.
CIKM-2017-HoangL #clustering #mining #performance: Highly Efficient Mining of Overlapping Clusters in Signed Weighted Networks (TAH, EPL), pp. 869–878.
CIKM-2017-HuPJL #classification #graph: Graph Ladder Networks for Network Classification (RH, SP, JJ0, GL), pp. 2103–2106.
CIKM-2017-JinZ #modelling #social: Emotions in Social Networks: Distributions, Patterns, and Models (SJ, RZ), pp. 1907–1916.
CIKM-2017-KhuranaASVS #hybrid: Hybrid BiLSTM-Siamese network for FAQ Assistance (PK, PA, GMS, LV, AS0), pp. 537–545.
CIKM-2017-LiDHTCL #learning: Attributed Network Embedding for Learning in a Dynamic Environment (JL, HD, XH, JT, YC, HL0), pp. 387–396.
CIKM-2017-LyuZZ #quality #similarity: Enhancing the Network Embedding Quality with Structural Similarity (TL, YZ, YZ), pp. 147–156.
CIKM-2017-MalmiGT #approach: Active Network Alignment: A Matching-Based Approach (EM, AG, ET), pp. 1687–1696.
CIKM-2017-MumtazW #identification: Identifying Top-K Influential Nodes in Networks (SM, XW), pp. 2219–2222.
CIKM-2017-NicosiaM #hybrid: Accurate Sentence Matching with Hybrid Siamese Networks (MN, AM), pp. 2235–2238.
CIKM-2017-ParkLC #recommendation: Deep Neural Networks for News Recommendations (KP, JL, JC), pp. 2255–2258.
CIKM-2017-PeiYSZBT #recommendation: Interacting Attention-gated Recurrent Networks for Recommendation (WP, JY0, ZS, JZ0, AB, DMJT), pp. 1459–1468.
CIKM-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.
CIKM-2017-RaoTHJL: Talking to Your TV: Context-Aware Voice Search with Hierarchical Recurrent Neural Networks (JR, FT, HH, OJ, JL), pp. 557–566.
CIKM-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.
CIKM-2017-SaravanouKVKG: Revealing the Hidden Links in Content Networks: An Application to Event Discovery (AS, IK, GV, VK, DG), pp. 2283–2286.
CIKM-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.
CIKM-2017-TayTH #analysis #memory management #sentiment: Dyadic Memory Networks for Aspect-based Sentiment Analysis (YT, LAT, SCH), pp. 107–116.
CIKM-2017-TayTPH #graph #multi #predict: Multi-Task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs (YT, LAT, MCP, SCH), pp. 1029–1038.
CIKM-2017-TengLW #detection #learning #multi #using: Anomaly Detection in Dynamic Networks using Multi-view Time-Series Hypersphere Learning (XT, YRL, XW), pp. 827–836.
CIKM-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.
CIKM-2017-TianC #interactive #visualisation: Visualizing Deep Neural Networks with Interaction of Super-pixels (ST, YC), pp. 2327–2330.
CIKM-2017-WangATL: Attributed Signed Network Embedding (SW, CCA, JT, HL0), pp. 137–146.
CIKM-2017-WangDYT #comprehension #mobile #predict #social: Understanding and Predicting Weight Loss with Mobile Social Networking Data (ZW, TD, DY, JT), pp. 1269–1278.
CIKM-2017-WangSLSZ0: Distant Meta-Path Similarities for Text-Based Heterogeneous Information Networks (CW, YS, HL, YS, MZ0, JH0), pp. 1629–1638.
CIKM-2017-WuUBG #detection: Conflict of Interest Declaration and Detection System in Heterogeneous Networks (SW, LHU, SSB, WG), pp. 2383–2386.
CIKM-2017-XiangJ #learning #multimodal: Common-Specific Multimodal Learning for Deep Belief Network (CX, XJ), pp. 2387–2390.
CIKM-2017-XuM #analysis #multimodal #named #semantics #sentiment: MultiSentiNet: A Deep Semantic Network for Multimodal Sentiment Analysis (NX, WM), pp. 2399–2402.
CIKM-2017-XuWXQ #classification #graph #recursion: Attentive Graph-based Recursive Neural Network for Collective Vertex Classification (QX, QW, CX, LQ), pp. 2403–2406.
CIKM-2017-YangWLZL #graph: From Properties to Links: Deep Network Embedding on Incomplete Graphs (DY, SW, CL, XZ0, ZL), pp. 367–376.
CIKM-2017-YuanWLL #detection: Spectrum-based Deep Neural Networks for Fraud Detection (SY, XW, JL, AL), pp. 2419–2422.
CIKM-2017-ZhangH #ambiguity #graph #using: Name Disambiguation in Anonymized Graphs using Network Embedding (BZ, MAH), pp. 1239–1248.
ECIR-2017-AletrasM #image #topic #using: Labeling Topics with Images Using a Neural Network (NA, AM), pp. 500–505.
ECIR-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.
ECIR-2017-ClosBWC #predict #social: Predicting Emotional Reaction in Social Networks (JC, AB, NW, GC), pp. 527–533.
ECIR-2017-HuangHC #detection: Irony Detection with Attentive Recurrent Neural Networks (YHH, HHH, HHC), pp. 534–540.
ECIR-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.
ICML-2017-AmosK #named #optimisation: OptNet: Differentiable Optimization as a Layer in Neural Networks (BA, JZK), pp. 136–145.
ICML-2017-AmosXK: Input Convex Neural Networks (BA, LX, JZK), pp. 146–155.
ICML-2017-ArjovskyCB #generative: Wasserstein Generative Adversarial Networks (MA, SC, LB), pp. 214–223.
ICML-2017-ArpitJBKBKMFCBL: A Closer Look at Memorization in Deep Networks (DA, SJ, NB, DK, EB, MSK, TM, AF, ACC, YB, SLJ), pp. 233–242.
ICML-2017-BalduzziMB #approximate #convergence: Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks (DB, BM, TBY), pp. 351–360.
ICML-2017-BelangerYM #energy #learning #predict: End-to-End Learning for Structured Prediction Energy Networks (DB, BY, AM), pp. 429–439.
ICML-2017-BolukbasiWDS #adaptation #performance: Adaptive Neural Networks for Efficient Inference (TB, JW0, OD, VS), pp. 527–536.
ICML-2017-ChoB #named: MEC: Memory-efficient Convolution for Deep Neural Network (MC, DB), pp. 815–824.
ICML-2017-CisseBGDU #robust: Parseval Networks: Improving Robustness to Adversarial Examples (MC, PB, EG, YND, NU), pp. 854–863.
ICML-2017-CortesGKMY #adaptation #learning #named: AdaNet: Adaptive Structural Learning of Artificial Neural Networks (CC, XG, VK, MM, SY), pp. 874–883.
ICML-2017-DauphinFAG #modelling: Language Modeling with Gated Convolutional Networks (YND, AF, MA, DG), pp. 933–941.
ICML-2017-FeldmanOR #graph #summary: Coresets for Vector Summarization with Applications to Network Graphs (DF, SO, DR), pp. 1117–1125.
ICML-2017-FinnAL #adaptation #performance: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks (CF, PA, SL), pp. 1126–1135.
ICML-2017-FoersterGSCS #architecture: Input Switched Affine Networks: An RNN Architecture Designed for Interpretability (JNF, JG, JSD, JC, DS), pp. 1136–1145.
ICML-2017-GaoFC #learning: Local-to-Global Bayesian Network Structure Learning (TG, KPF, MC), pp. 1193–1202.
ICML-2017-GravesBMMK #automation #education #learning: Automated Curriculum Learning for Neural Networks (AG, MGB, JM, RM, KK), pp. 1311–1320.
ICML-2017-GuoPSW #on the: On Calibration of Modern Neural Networks (CG, GP, YS0, KQW), pp. 1321–1330.
ICML-2017-GygliNA: Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs (MG, MN0, AA), pp. 1341–1351.
ICML-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.
ICML-2017-HuQ #memory management: State-Frequency Memory Recurrent Neural Networks (HH, GJQ), pp. 1568–1577.
ICML-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.
ICML-2017-KalchbrennerOSD #video: Video Pixel Networks (NK, AvdO, KS, ID, OV, AG, KK), pp. 1771–1779.
ICML-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.
ICML-2017-KimCKLK #generative #learning: Learning to Discover Cross-Domain Relations with Generative Adversarial Networks (TK, MC, HK, JKL, JK), pp. 1857–1865.
ICML-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.
ICML-2017-LiG: Dropout Inference in Bayesian Neural Networks with Alpha-divergences (YL, YG), pp. 2052–2061.
ICML-2017-LivniCG #infinity #kernel #learning: Learning Infinite Layer Networks Without the Kernel Trick (RL, DC, AG), pp. 2198–2207.
ICML-2017-LongZ0J #adaptation #learning: Deep Transfer Learning with Joint Adaptation Networks (ML, HZ, JW0, MIJ), pp. 2208–2217.
ICML-2017-LouizosW #multi #normalisation: Multiplicative Normalizing Flows for Variational Bayesian Neural Networks (CL, MW), pp. 2218–2227.
ICML-2017-Luo #architecture #learning: Learning Deep Architectures via Generalized Whitened Neural Networks (PL0), pp. 2238–2246.
ICML-2017-MaystreG17a #identification #named: ChoiceRank: Identifying Preferences from Node Traffic in Networks (LM, MG), pp. 2354–2362.
ICML-2017-McGillP #how: Deciding How to Decide: Dynamic Routing in Artificial Neural Networks (MM, PP), pp. 2363–2372.
ICML-2017-MeschederNG #generative: Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks (LMM, SN, AG), pp. 2391–2400.
ICML-2017-MhammediHRB #orthogonal #performance #using: Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using Householder Reflections (ZM, ADH, AR, JB0), pp. 2401–2409.
ICML-2017-MolchanovAV: Variational Dropout Sparsifies Deep Neural Networks (DM, AA, DPV), pp. 2498–2507.
ICML-2017-MunkhdalaiY: Meta Networks (TM, HY0), pp. 2554–2563.
ICML-2017-NeilLDL: Delta Networks for Optimized Recurrent Network Computation (DN, JL, TD, SCL), pp. 2584–2593.
ICML-2017-NguyenH: The Loss Surface of Deep and Wide Neural Networks (QN0, MH0), pp. 2603–2612.
ICML-2017-PenningtonB #geometry #matrix #random: Geometry of Neural Network Loss Surfaces via Random Matrix Theory (JP, YB), pp. 2798–2806.
ICML-2017-RaghuPKGS #on the #power of: On the Expressive Power of Deep Neural Networks (MR, BP, JMK, SG, JSD), pp. 2847–2854.
ICML-2017-RitterBSB #bias #case study: Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study (SR, DGTB, AS, MMB), pp. 2940–2949.
ICML-2017-SafranS #approximate #trade-off: Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks (IS, OS), pp. 2979–2987.
ICML-2017-SakrKS #precise: Analytical Guarantees on Numerical Precision of Deep Neural Networks (CS, YK0, NRS), pp. 3007–3016.
ICML-2017-ScamanBBLM #algorithm #distributed #optimisation: Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks (KS, FRB, SB, YTL, LM), pp. 3027–3036.
ICML-2017-SundararajanTY #axiom: Axiomatic Attribution for Deep Networks (MS, AT, QY), pp. 3319–3328.
ICML-2017-Telgarsky: Neural Networks and Rational Functions (MT), pp. 3387–3393.
ICML-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.
ICML-2017-TompsonSSP #simulation: Accelerating Eulerian Fluid Simulation With Convolutional Networks (JT, KS, PS, KP), pp. 3424–3433.
ICML-2017-VezhnevetsOSHJS #learning: FeUdal Networks for Hierarchical Reinforcement Learning (ASV, SO, TS, NH, MJ, DS, KK), pp. 3540–3549.
ICML-2017-VorontsovTKP #dependence #learning #on the #orthogonal: On orthogonality and learning recurrent networks with long term dependencies (EV, CT, SK, CP), pp. 3570–3578.
ICML-2017-YangKT #classification #video: Tensor-Train Recurrent Neural Networks for Video Classification (YY, DK, VT), pp. 3891–3900.
ICML-2017-YoonH: Combined Group and Exclusive Sparsity for Deep Neural Networks (JY, SJH), pp. 3958–3966.
ICML-2017-ZhangLW: Convexified Convolutional Neural Networks (YZ0, PL, MJW), pp. 4044–4053.
ICML-2017-ZhangZZHZ #distributed #learning #online: Projection-free Distributed Online Learning in Networks (WZ0, PZ, WZ0, SCHH, TZ), pp. 4054–4062.
ICML-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.
ICML-2017-ZhongS0BD: Recovery Guarantees for One-hidden-layer Neural Networks (KZ, ZS, PJ0, PLB, ISD), pp. 4140–4149.
ICML-2017-ZillySKS: Recurrent Highway Networks (JGZ, RKS, JK, JS), pp. 4189–4198.
KDD-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.
KDD-2017-AvinLNP #bound: Improved Degree Bounds and Full Spectrum Power Laws in Preferential Attachment Networks (CA, ZL, YN, DP), pp. 45–53.
KDD-2017-BaiWT0D: Unsupervised Network Discovery for Brain Imaging Data (ZB, PBW, AET, FW0, ID), pp. 55–64.
KDD-2017-BaytasXZWJZ #type system: Patient Subtyping via Time-Aware LSTM Networks (IMB, CX, XZ, FW0, AKJ0, JZ), pp. 65–74.
KDD-2017-ChengLL #feature model #social: Unsupervised Feature Selection in Signed Social Networks (KC, JL, HL0), pp. 777–786.
KDD-2017-DadkhahiM #detection #embedded #learning: Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices (HD, BMM), pp. 1773–1781.
KDD-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.
KDD-2017-DongCS #learning #named #representation #scalability: metapath2vec: Scalable Representation Learning for Heterogeneous Networks (YD, NVC, AS), pp. 135–144.
KDD-2017-DongJXC #case study: Structural Diversity and Homophily: A Study Across More Than One Hundred Big Networks (YD, RAJ, JX, NVC), pp. 807–816.
KDD-2017-EikmeierG: Revisiting Power-law Distributions in Spectra of Real World Networks (NE, DFG), pp. 817–826.
KDD-2017-GuSG #co-evolution #evolution #migration #social: The Co-Evolution Model for Social Network Evolving and Opinion Migration (YG, YS, JG), pp. 175–184.
KDD-2017-HallacPBL #visual notation: Network Inference via the Time-Varying Graphical Lasso (DH, YP, SPB, JL), pp. 205–213.
KDD-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.
KDD-2017-LiMGK #interactive: A Context-aware Attention Network for Interactive Question Answering (HL, MRM, YG, AK), pp. 927–935.
KDD-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.
KDD-2017-MottiniA #pointer #predict #using: Deep Choice Model Using Pointer Networks for Airline Itinerary Prediction (AM, RAA), pp. 1575–1583.
KDD-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.
KDD-2017-SaveskiPSDGXA #detection #random: Detecting Network Effects: Randomizing Over Randomized Experiments (MS, JPA, GSJ, WD, SG, YX, EMA), pp. 1027–1035.
KDD-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.
KDD-2017-ShenHYSLC #on the #online #social: On Finding Socially Tenuous Groups for Online Social Networks (CYS, LHH, DNY, HHS, WCL, MSC), pp. 415–424.
KDD-2017-ShiCZG0 #named #probability: PReP: Path-Based Relevance from a Probabilistic Perspective in Heterogeneous Information Networks (YS, PWC, HZ, HG, JH0), pp. 425–434.
KDD-2017-SinghSGMC #evolution #modelling: Relay-Linking Models for Prominence and Obsolescence in Evolving Networks (MS0, RS, PG, AM0, SC), pp. 1077–1086.
KDD-2017-WangGZOXLG #detection: Adversary Resistant Deep Neural Networks with an Application to Malware Detection (QW, WG, KZ, AGOI, XX, XL, CLG), pp. 1145–1153.
KDD-2017-WangHCLYR #mining: Structural Deep Brain Network Mining (SW, LH0, BC, CTL, PSY, ABR), pp. 475–484.
KDD-2017-YeZMPB #learning: Learning from Labeled and Unlabeled Vertices in Networks (WY0, LZ, DM, CP, CB), pp. 1265–1274.
KDD-2017-YinCZ #comprehension #design #named #sequence: DeepProbe: Information Directed Sequence Understanding and Chatbot Design via Recurrent Neural Networks (ZY, KhC, RZ), pp. 2131–2139.
KDD-2017-YouX0T #education #learning #multi: Learning from Multiple Teacher Networks (SY, CX0, CX0, DT), pp. 1285–1294.
KDD-2017-ZhaoYLSL #recommendation: Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks (HZ, QY, JL, YS, DLL), pp. 635–644.
ECMFA-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.
PLDI-2017-BeckettMMPW #synthesis: Network configuration synthesis with abstract topologies (RB, RM, TDM, JP, DW), pp. 437–451.
POPL-2017-SmolkaKFK0 #probability #semantics: Cantor meets scott: semantic foundations for probabilistic networks (SS, PK0, NF, DK, AS0), pp. 557–571.
POPL-2017-SubramanianDA #multitenancy #named #overview: Genesis: synthesizing forwarding tables in multi-tenant networks (KS, LD, AA), pp. 572–585.
ASE-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.
ASE-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-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-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.
ASPLOS-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.
ASPLOS-2017-HuSL #towards: Towards “Full Containerization” in Containerized Network Function Virtualization (YH0, MS, TL0), pp. 467–481.
ASPLOS-2017-LesokhinERSGLBA #fault: Page Fault Support for Network Controllers (IL, HE, SR, GS, SG, LL, MBY, NA, DT), pp. 449–466.
ASPLOS-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.
CASE-2017-AskinH #automation #estimation: Automated lead time estimation for manufacturing networks with dynamic demand (RGA, GJH), pp. 994–999.
CASE-2017-CuiHD #collaboration #problem: 4PL collaborative routing customization problem on the dynamic networks (YC, MH0, QD), pp. 1345–1349.
CASE-2017-CuiVZBB #adaptation #architecture #self: A software architecture supporting self-adaptation of wireless control networks (YC, RMV, XZ, JB, ESB), pp. 346–351.
CASE-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.
CASE-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.
CASE-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.
CASE-2017-LiuJZ #metric #optimisation: An evacuation guider location optimization method based on road network centrality measures (ZL, QSJ, HZ), pp. 838–843.
CASE-2017-LiuLQLD #estimation: State estimation for BAM neural networks with mixed time delays (GL, CL, JQ, XL, JD), pp. 1506–1509.
CASE-2017-TianJW #sequence #social: Opinion containment in social networks over issue sequences (YT, GJ, LW0), pp. 1374–1379.
CASE-2017-Wang: Regulation by competing: A hidden layer of gene regulatory networks (XW), p. 96.
CASE-2017-WangWQL #constraints #metaprogramming #social: From micro to macro: Propagated constraints in social networks (CW, YW, TQ, HL), pp. 1534–1539.
CASE-2017-WuXS #protocol #trust: Trust-based protocol for securing routing in opportunistic networks (XW, JX, JS), pp. 434–439.
CASE-2017-YangLXC #algebra #logic: Controllability of dynamic-algebraic mix-valued logical control networks (LY, BL, RX, JC), pp. 171–176.
CASE-2017-ZhangJ #bias #distributed #metric: Target tracking over distributed sensor networks by polar measurements with time-varying bias (CZ, YJ), pp. 429–433.
CAV-2017-HuangKWW #safety #verification: Safety Verification of Deep Neural Networks (XH0, MK, SW, MW), pp. 3–29.
CAV-2017-KatzBDJK #named #performance #smt #verification: Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks (GK, CWB, DLD, KJ, MJK), pp. 97–117.
CAV-2017-CardelliCFKLPW #synthesis: Syntax-Guided Optimal Synthesis for Chemical Reaction Networks (LC, MC0, MF, MZK, LL, NP, MW), pp. 375–395.
CAV-2017-McClurgHC #source code #synthesis: Synchronization Synthesis for Network Programs (JM, HH, PC), pp. 301–321.
QoSA-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.
EDM-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.
EDM-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.
EDM-2016-WilsonKHE #estimation: Back to the basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation (KHW, YK, BH, CE), pp. 539–544.
FM-2016-CimattiMS #automaton #hybrid: From Electrical Switched Networks to Hybrid Automata (AC, SM, MS), pp. 164–181.
AIIDE-2016-SinghDHJPM #using: Recognizing Actions in Motion Trajectories Using Deep Neural Networks (KYS, NMD, CPH, MJ, KP, BM), pp. 211–217.
CHI-PLAY-2016-CollinsCL #exclamation #game studies #student: Say Cheese!: Games for Successful Academic and Student Networking (EIMC, ALC, FJL), pp. 105–115.
CHI-PLAY-2016-WellsCLMGS #data mining #mining: Mining for Gold (and Platinum): PlayStation Network Data Mining (LW, AJCS, IJL, LM, BG, KdS), pp. 304–312.
CIG-2016-SoemersW #game studies #reuse #video: Hierarchical Task Network Plan Reuse for video games (DJNJS, MHMW), pp. 1–8.
CIG-2016-StanescuBHB #game studies #realtime #using: Evaluating real-time strategy game states using convolutional neural networks (MS, NAB, AH, MB), pp. 1–7.
DiGRA-FDG-2016-PaavilainenAK #game studies #overview #social: Review of Social Features in Social Network Games (JP, KA, HK).
CIKM-2016-0002AK #distance #query: Fully Dynamic Shortest-Path Distance Query Acceleration on Massive Networks (TH0, TA, KiK), pp. 1533–1542.
CIKM-2016-AnwarLV0 #evolution: Tracking the Evolution of Congestion in Dynamic Urban Road Networks (TA, CL, HLV, MSI0), pp. 2323–2328.
CIKM-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.
CIKM-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.
CIKM-2016-ChenZH: Incorporate Group Information to Enhance Network Embedding (JC, QZ0, XH), pp. 1901–1904.
CIKM-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.
CIKM-2016-JiangL: Forecasting Geo-sensor Data with Participatory Sensing Based on Dropout Neural Network (JYJ, CTL), pp. 2033–2036.
CIKM-2016-JiangYCZY #privacy #query #reachability: Privacy-Preserving Reachability Query Services for Massive Networks (JJ, PY, BC, ZZ, XY0), pp. 145–154.
CIKM-2016-KasneciG #linear #named: LICON: A Linear Weighting Scheme for the Contribution ofInput Variables in Deep Artificial Neural Networks (GK, TG), pp. 45–54.
CIKM-2016-LiKZH #classification #on the: On Transductive Classification in Heterogeneous Information Networks (XL, BK, YZ, ZH0), pp. 811–820.
CIKM-2016-LiTCELB #interactive #named #optimisation: TEAMOPT: Interactive Team Optimization in Big Networks (LL, HT, NC, KE, YRL, NB), pp. 2485–2487.
CIKM-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.
CIKM-2016-ManshaKKA #identification #self #speech: A Self-Organizing Map for Identifying InfluentialCommunities in Speech-based Networks (SM, FK, AK, AA), pp. 1965–1968.
CIKM-2016-MaSCYKV #distributed #performance #query: Query Answering Efficiency in Expert Networks Under Decentralized Search (LM0, MS, DC, XY, SK, MV), pp. 2119–2124.
CIKM-2016-NegiC #predict #social: Link Prediction in Heterogeneous Social Networks (SN, SC), pp. 609–617.
CIKM-2016-RaoHL #estimation: Noise-Contrastive Estimation for Answer Selection with Deep Neural Networks (JR, HH, JJL), pp. 1913–1916.
CIKM-2016-RongZC #approach: A Model-Free Approach to Infer the Diffusion Network from Event Cascade (YR, QZ, HC), pp. 1653–1662.
CIKM-2016-RossiZ #multi #scalability: Leveraging Multiple GPUs and CPUs for Graphlet Counting in Large Networks (RAR, RZ0), pp. 1783–1792.
CIKM-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.
CIKM-2016-SatyaLLTZ #online #social: Uncovering Fake Likers in Online Social Networks (PRBS, KL, DL0, TT, J(Z), pp. 2365–2370.
CIKM-2016-SongHL #social: Targeted Influence Maximization in Social Networks (CS, WH, MLL), pp. 1683–1692.
CIKM-2016-TangCSTV0: BigNet 2016: First Workshop on Big Network Analytics (JT0, KC, ZS, HT, MV, YY0), pp. 2505–2506.
CIKM-2016-TanWX #approach #recommendation: A Neural Network Approach to Quote Recommendation in Writings (JT, XW0, JX), pp. 65–74.
CIKM-2016-UfimtsevSMB #comprehension #metric: Understanding Stability of Noisy Networks through Centrality Measures and Local Connections (VU, SS, AM0, SB), pp. 2347–2352.
CIKM-2016-ZengZMZW #clustering #predict: Exploiting Cluster-based Meta Paths for Link Prediction in Signed Networks (JZ, KZ0, XM, FZ, HW), pp. 1905–1908.
CIKM-2016-ZhangGWHH #predict #twitter: Retweet Prediction with Attention-based Deep Neural Network (QZ0, YG, JW, HH, XH), pp. 75–84.
CIKM-2016-ZhangTL #clustering #multi: Clustering Speed in Multi-lane Traffic Networks (BZ, GT, FL), pp. 2045–2048.
CIKM-2016-ZhangYZZ #classification #matrix: Collective Classification via Discriminative Matrix Factorization on Sparsely Labeled Networks (DZ, JY, XZ, CZ), pp. 1563–1572.
ICML-2016-AllamanisPS #source code #summary: A Convolutional Attention Network for Extreme Summarization of Source Code (MA, HP, CAS), pp. 2091–2100.
ICML-2016-AlmahairiBCZLC #capacity: Dynamic Capacity Networks (AA, NB, TC, YZ, HL, ACC), pp. 2549–2558.
ICML-2016-ArjovskySB #evolution: Unitary Evolution Recurrent Neural Networks (MA, AS, YB), pp. 1120–1128.
ICML-2016-ArpitZKG #normalisation #parametricity: Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks (DA, YZ, BUK, VG), pp. 1168–1176.
ICML-2016-BalduzziG: Strongly-Typed Recurrent Neural Networks (DB, MG), pp. 1292–1300.
ICML-2016-BelangerM #energy #predict: Structured Prediction Energy Networks (DB, AM), pp. 983–992.
ICML-2016-BlondelIFU #algorithm #performance #polynomial: Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms (MB, MI, AF, NU), pp. 850–858.
ICML-2016-CohenS: Convolutional Rectifier Networks as Generalized Tensor Decompositions (NC, AS), pp. 955–963.
ICML-2016-CohenW: Group Equivariant Convolutional Networks (TC, MW), pp. 2990–2999.
ICML-2016-CouilletWAS #approach #matrix #random: A Random Matrix Approach to Echo-State Neural Networks (RC, GW, HTA, HS), pp. 517–525.
ICML-2016-DielemanFK #symmetry: Exploiting Cyclic Symmetry in Convolutional Neural Networks (SD, JDF, KK), pp. 1889–1898.
ICML-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.
ICML-2016-HenaffSL #orthogonal: Recurrent Orthogonal Networks and Long-Memory Tasks (MH, AS, YL), pp. 2034–2042.
ICML-2016-KordaSL #clustering #distributed #linear: Distributed Clustering of Linear Bandits in Peer to Peer Networks (NK, BS, SL), pp. 1301–1309.
ICML-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.
ICML-2016-LinTA #fixpoint: Fixed Point Quantization of Deep Convolutional Networks (DDL, SST, VSA), pp. 2849–2858.
ICML-2016-LiOW #multi: Multi-Bias Non-linear Activation in Deep Neural Networks (HL, WO, XW0), pp. 221–229.
ICML-2016-LiuSSF #learning #markov: Structure Learning of Partitioned Markov Networks (SL0, TS, MS, KF), pp. 439–448.
ICML-2016-LiuWYY: Large-Margin Softmax Loss for Convolutional Neural Networks (WL, YW, ZY, MY0), pp. 507–516.
ICML-2016-NiepertAK #graph #learning: Learning Convolutional Neural Networks for Graphs (MN, MA, KK), pp. 2014–2023.
ICML-2016-OordKK: Pixel Recurrent Neural Networks (AvdO, NK, KK), pp. 1747–1756.
ICML-2016-OswalCRRN #learning #similarity: Representational Similarity Learning with Application to Brain Networks (UO, CRC, MALR, TTR, RDN), pp. 1041–1049.
ICML-2016-PaigeW #modelling #monte carlo #visual notation: Inference Networks for Sequential Monte Carlo in Graphical Models (BP, FDW), pp. 3040–3049.
ICML-2016-PanS: Expressiveness of Rectifier Networks (XP, VS), pp. 2427–2435.
ICML-2016-PezeshkiFBCB #architecture: Deconstructing the Ladder Network Architecture (MP, LF, PB, ACC, YB), pp. 2368–2376.
ICML-2016-SafranS #on the #quality: On the Quality of the Initial Basin in Overspecified Neural Networks (IS, OS), pp. 774–782.
ICML-2016-SantoroBBWL: Meta-Learning with Memory-Augmented Neural Networks (AS, SB, MB, DW, TPL), pp. 1842–1850.
ICML-2016-ShangSAL #comprehension #linear: Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units (WS, KS, DA, HL), pp. 2217–2225.
ICML-2016-SongGC #learning #sequence: Factored Temporal Sigmoid Belief Networks for Sequence Learning (JS, ZG, LC), pp. 1272–1281.
ICML-2016-SongSZU: Training Deep Neural Networks via Direct Loss Minimization (YS, AGS, RSZ, RU), pp. 2169–2177.
ICML-2016-TaylorBXSPG #approach #scalability: Training Neural Networks Without Gradients: A Scalable ADMM Approach (GT, RB, ZX0, BS, ABP, TG), pp. 2722–2731.
ICML-2016-UlyanovLVL #image #synthesis: Texture Networks: Feed-forward Synthesis of Textures and Stylized Images (DU, VL, AV, VSL), pp. 1349–1357.
ICML-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.
ICML-2016-WangSHHLF #architecture #learning: Dueling Network Architectures for Deep Reinforcement Learning (ZW0, TS, MH, HvH, ML, NdF), pp. 1995–2003.
ICML-2016-WeiWRC #morphism: Network Morphism (TW, CW, YR, CWC), pp. 564–572.
ICML-2016-XiongMS #memory management #visual notation: Dynamic Memory Networks for Visual and Textual Question Answering (CX, SM, RS), pp. 2397–2406.
ICML-2016-ZhangLJ #polynomial: L1-regularized Neural Networks are Improperly Learnable in Polynomial Time (YZ0, JDL, MIJ), pp. 993–1001.
ICML-2016-ZhangLL #classification #image #scalability: Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification (YZ, KL, HL), pp. 612–621.
ICML-2016-ZhaoAGA: Collapsed Variational Inference for Sum-Product Networks (HZ0, TA, GJG, BA), pp. 1310–1318.
ICPR-2016-AntonyMOM #using: Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks (JA, KM, NEO, KM), pp. 1195–1200.
ICPR-2016-BSSH #approach #classification #using: HEp-2 cell classification using artificial neural network approach (DB, KS, NH), pp. 84–89.
ICPR-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.
ICPR-2016-CapuaNP #detection #social: Unsupervised cyber bullying detection in social networks (MDC, EDN, AP), pp. 432–437.
ICPR-2016-ChaiLYLC #gesture #recognition #scalability: Two streams Recurrent Neural Networks for Large-Scale Continuous Gesture Recognition (XC, ZL, FY, ZL, XC), pp. 31–36.
ICPR-2016-ChenQYJ #bidirectional: Face alignment with Cascaded Bidirectional LSTM Neural Networks (YC, JQ, JY0, ZJ), pp. 313–318.
ICPR-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.
ICPR-2016-ChoiKPS #detection #multi: Multi-spectral pedestrian detection based on accumulated object proposal with fully convolutional networks (HC, SK, KP, KS), pp. 621–626.
ICPR-2016-ChowdhuryBMKS #image #performance #retrieval #using: An efficient radiographic Image Retrieval system using Convolutional Neural Network (MC, SRB, RM, MKK, ÖS), pp. 3134–3139.
ICPR-2016-CorniaBSC #multi #predict: A deep multi-level network for saliency prediction (MC, LB, GS0, RC), pp. 3488–3493.
ICPR-2016-DuWZH #markov #recognition: Deep neural network based hidden Markov model for offline handwritten Chinese text recognition (JD, ZRW, JFZ, JSH), pp. 3428–3433.
ICPR-2016-FengLXYM #traversal: Face hallucination by deep traversal network (ZXF, JHL, XX, DY, LM), pp. 3276–3281.
ICPR-2016-GaoYGC #approach #constraints #using: Bayesian approach to learn Bayesian networks using data and constraints (XGG, YY, ZgG, DQC0), pp. 3667–3672.
ICPR-2016-GhaderiA #learning: Selective unsupervised feature learning with Convolutional Neural Network (S-CNN) (AG, VA), pp. 2486–2490.
ICPR-2016-GrantSZG #predict #visualisation: Predicting and visualizing psychological attributions with a deep neural network (EG, SS, MZ, MvG), pp. 1–6.
ICPR-2016-GwonCK: Deep Sparse-coded Network (DSN) (YG, MC, HTK), pp. 2610–2615.
ICPR-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.
ICPR-2016-HasegawaH #classification #image #named #using: PLSNet: A simple network using Partial Least Squares regression for image classification (RH, KH), pp. 1601–1606.
ICPR-2016-HuangY #3d #using: Point cloud labeling using 3D Convolutional Neural Network (JH0, SY), pp. 2670–2675.
ICPR-2016-JhuangLT #3d #using #verification: Face verification with three-dimensional point cloud by using deep belief networks (DHJ, DTL, CHT), pp. 1430–1435.
ICPR-2016-JiaSZY #classification: Deep convolutional neural network based HEp-2 cell classification (XJ, LS, XZ, SY), pp. 77–80.
ICPR-2016-KabkabHC #on the #performance: On the size of Convolutional Neural Networks and generalization performance (MK, EMH, RC), pp. 3572–3577.
ICPR-2016-KalraSRT #learning #using: Learning opposites using neural networks (SK, AS, SR, HRT), pp. 1213–1218.
ICPR-2016-KimP #approach #detection #using: A shape preserving approach for salient object detection using convolutional neural networks (JK, VP), pp. 609–614.
ICPR-2016-KimP16a #using: Discovering characteristic landmarks on ancient coins using convolutional networks (JK, VP), pp. 1595–1600.
ICPR-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.
ICPR-2016-LiSZY #classification: HEp-2 specimen classification with fully convolutional network (YL, LS, XZ, SY), pp. 96–100.
ICPR-2016-LittwinW #complexity #multi: Complexity of multiverse networks and their multilayer generalization (EL, LW), pp. 372–377.
ICPR-2016-Liu16a #classification #learning #multi #scalability: Hierarchical learning for large multi-class network classification (LL), pp. 2307–2312.
ICPR-2016-LiuGX #constraints #synthesis: Texture synthesis through convolutional neural networks and spectrum constraints (GL0, YG, GSX), pp. 3234–3239.
ICPR-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.
ICPR-2016-LyuSZB #image #multi: Distinguishing text/non-text natural images with Multi-Dimensional Recurrent Neural Networks (PL, BS, CZ, XB), pp. 3981–3986.
ICPR-2016-McCaneS #performance: Deep networks are efficient for circular manifolds (BM, LS), pp. 3464–3469.
ICPR-2016-MeiDSB #identification: Scene text script identification with Convolutional Recurrent Neural Networks (JM, LD, BS, XB), pp. 4053–4058.
ICPR-2016-MelekhovKR #image: Siamese network features for image matching (IM, JK, ER), pp. 378–383.
ICPR-2016-MinelloTH #evolution #quantum: Quantum thermodynamics of time evolving networks (GM, AT, ERH), pp. 1536–1541.
ICPR-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.
ICPR-2016-NairKNG #documentation #segmentation #using: Segmentation of highly unstructured handwritten documents using a neural network technique (RRN, BUK, IN, VG), pp. 1291–1296.
ICPR-2016-NieZJ #data transformation #representation: Latent regression Bayesian network for data representation (SN, YZ, QJ), pp. 3494–3499.
ICPR-2016-NookaCVSP #adaptation #classification: Adaptive hierarchical classification networks (SPN, SC, KV, SS, RWP), pp. 3578–3583.
ICPR-2016-PandaDR #multi #summary #video: Video summarization in a multi-view camera network (RP, AD, AKRC), pp. 2971–2976.
ICPR-2016-PangN #3d #detection #multi: 3D point cloud object detection with multi-view convolutional neural network (GP, UN), pp. 585–590.
ICPR-2016-PengRP #learning #recognition #using: Learning face recognition from limited training data using deep neural networks (XP, NKR, SP), pp. 1442–1447.
ICPR-2016-PengZ: Mutual information-based RBM neural networks (KHP, HZ), pp. 2458–2463.
ICPR-2016-Pham0PV #performance: Faster training of very deep networks via p-norm gates (TP, TT0, DQP, SV), pp. 3542–3547.
ICPR-2016-PuZZ #multi: Structure and appearance preserving network flow for multi-object tracking (SP, HZ, KZ), pp. 1804–1808.
ICPR-2016-RoyDB #classification #documentation #image: Generalized stacking of layerwise-trained Deep Convolutional Neural Networks for document image classification (SR, AD, UB), pp. 1273–1278.
ICPR-2016-RoyTL #learning: Context-regularized learning of fully convolutional networks for scene labeling (AR, ST, LJL), pp. 3751–3756.
ICPR-2016-ShankarDG #learning: Reinforcement Learning via Recurrent Convolutional Neural Networks (TS, SKD, PG), pp. 2592–2597.
ICPR-2016-StunerCP #recognition #word: Cascading BLSTM networks for handwritten word recognition (BS, CC0, TP), pp. 3416–3421.
ICPR-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.
ICPR-2016-SunHLK #learning #multi #recognition: Multiple Instance Learning Convolutional Neural Networks for object recognition (MS, TXH, MCL, AKR), pp. 3270–3275.
ICPR-2016-TabernikKWL #composition #towards: Towards deep compositional networks (DT, MK, JLW, AL), pp. 3470–3475.
ICPR-2016-Teerapittayanon #named #performance: BranchyNet: Fast inference via early exiting from deep neural networks (ST, BM, HTK), pp. 2464–2469.
ICPR-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.
ICPR-2016-Triantafyllidou #detection #incremental #learning: Face detection based on deep convolutional neural networks exploiting incremental facial part learning (DT, AT), pp. 3560–3565.
ICPR-2016-Uchida0O: Coupled convolution layer for convolutional neural network (KU, MT0, MO), pp. 3548–3553.
ICPR-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.
ICPR-2016-VargaS #automation #image: Fully automatic image colorization based on Convolutional Neural Network (DV, TS), pp. 3691–3696.
ICPR-2016-WangB #predict: Link prediction via Supervised Dynamic Network Formation (YW0, LB0), pp. 4160–4165.
ICPR-2016-WangLLGTO #gesture #recognition #scalability #using: Large-scale Isolated Gesture Recognition using Convolutional Neural Networks (PW, WL, SL, ZG, CT, PO), pp. 7–12.
ICPR-2016-WangLLZGO #gesture #recognition #scalability #using: Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks (PW, WL, SL, YZ, ZG, PO), pp. 13–18.
ICPR-2016-WangLP #visual notation: Finetuning Convolutional Neural Networks for visual aesthetics (YW, YL, FP), pp. 3554–3559.
ICPR-2016-WangWH #analysis #using: Network entropy analysis using the Maxwell-Boltzmann partition function (JW, RCW0, ERH), pp. 1321–1326.
ICPR-2016-Williams #classification #using: Underwater target classification in synthetic aperture sonar imagery using deep convolutional neural networks (DPW), pp. 2497–2502.
ICPR-2016-XieSJFZ #recognition: Fully convolutional recurrent network for handwritten Chinese text recognition (ZX, ZS, LJ, ZF, SZ), pp. 4011–4016.
ICPR-2016-XuT #3d #learning: Beam search for learning a deep Convolutional Neural Network of 3D shapes (XX, ST), pp. 3506–3511.
ICPR-2016-Yamada #detection: Pedestrian detection with a resolution-aware convolutional network (KY), pp. 591–596.
ICPR-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.
ICPR-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.
ICPR-2016-YangN #detection #multi: A multi-scale cascade fully convolutional network face detector (ZY, RN), pp. 633–638.
ICPR-2016-YangSZ #detection: A joint facial point detection method of deep convolutional network and shape regression (TY, CS, NZ), pp. 543–548.
ICPR-2016-Ye0L #3d #retrieval #sketching: 3D sketch-based 3D model retrieval with convolutional neural network (YY, BL0, YL), pp. 2936–2941.
ICPR-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.
ICPR-2016-ZhangGST #recognition: Application of pronunciation knowledge on phoneme recognition by LSTM neural network (BZ, YG, YS, BT), pp. 2906–2911.
ICPR-2016-ZhangLQ #using: Wake-up-word spotting using end-to-end deep neural network system (SZ, WL, YQ0), pp. 2878–2883.
ICPR-2016-ZhangQ #adaptation #agile #modelling: Rapid feature space MLLR speaker adaptation for deep neural network acoustic modeling (SZ, YQ0), pp. 2889–2894.
ICPR-2016-ZhengCZZL #independence #using: Text-independent voice conversion using deep neural network based phonetic level features (HZ, WC, TZ, SZ, ML0), pp. 2872–2877.
ICPR-2016-ZhongZYL #recognition: Handwritten Chinese character recognition with spatial transformer and deep residual networks (ZZ, XYZ, FY, CLL), pp. 3440–3445.
ICPR-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.
ICPR-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.
KDD-2016-ArbourGJ: Inferring Network Effects from Observational Data (DTA, DG, DDJ), pp. 715–724.
KDD-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.
KDD-2016-ChangZTYCHH #learning #streaming: Positive-Unlabeled Learning in Streaming Networks (SC, YZ0, JT, DY, YC, MAHJ, TSH), pp. 755–764.
KDD-2016-Chayes #estimation #machine learning #modelling: Graphons and Machine Learning: Modeling and Estimation of Sparse Massive Networks (JTC), p. 1.
KDD-2016-ChenTXYH #dependence #multi #named #performance: FASCINATE: Fast Cross-Layer Dependency Inference on Multi-layered Networks (CC0, HT, LX, LY, QH), pp. 765–774.
KDD-2016-ChenWTWC: Compressing Convolutional Neural Networks in the Frequency Domain (WC, JTW, ST, KQW, YC), pp. 1475–1484.
KDD-2016-ChuWPWZC: Finding Gangs in War from Signed Networks (LC, ZW, JP, JW, ZZ, EC), pp. 1505–1514.
KDD-2016-CoskunGK #performance #proximity #query: Efficient Processing of Network Proximity Queries via Chebyshev Acceleration (MC, AG, MK), pp. 1515–1524.
KDD-2016-DengSDZYL #predict: Latent Space Model for Road Networks to Predict Time-Varying Traffic (DD, CS, UD, LZ, RY, YL0), pp. 1525–1534.
KDD-2016-Freitas #composition #learning: Learning to Learn and Compositionality with Deep Recurrent Neural Networks: Learning to Learn and Compositionality (NdF), p. 3.
KDD-2016-GroverL #learning #named #scalability: node2vec: Scalable Feature Learning for Networks (AG, JL), pp. 855–864.
KDD-2016-GuoLI #approximate: Convolutional Neural Networks for Steady Flow Approximation (XG, WL, FI), pp. 481–490.
KDD-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.
KDD-2016-HuangZCSML #scalability: Meta Structure: Computing Relevance in Large Heterogeneous Information Networks (ZH0, YZ, RC, YS, NM, XL), pp. 1595–1604.
KDD-2016-Li0TFT #named: QUINT: On Query-Specific Optimal Networks (LL, YY0, JT0, WF0, HT), pp. 985–994.
KDD-2016-LiuPLTCL #online #social: Audience Expansion for Online Social Network Advertising (HL, DP, KL, MT, FC, CL), pp. 165–174.
KDD-2016-NandanwarM #classification: Structural Neighborhood Based Classification of Nodes in a Network (SN, MNM), pp. 1085–1094.
KDD-2016-PerozziSST #recommendation: When Recommendation Goes Wrong: Anomalous Link Discovery in Recommendation Networks (BP, MS, JS, MT), pp. 569–578.
KDD-2016-Robles-GrandaMN #generative #modelling: Sampling of Attributed Networks from Hierarchical Generative Models (PRG, SM, JN), pp. 1155–1164.
KDD-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.
KDD-2016-WangC0: Structural Deep Network Embedding (DW, PC0, WZ0), pp. 1225–1234.
KDD-2016-XuYYXZ #detection: Talent Circle Detection in Job Transition Networks (HX, ZY0, JY, HX, HZ), pp. 655–664.
KDD-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.
KDD-2016-YuCG #identification #social: Identifying Decision Makers from Professional Social Networks (SY, EC, AG), pp. 333–342.
KDD-2016-ZangCF #social: Beyond Sigmoids: The NetTide Model for Social Network Growth, and Its Applications (CZ, PC0, CF), pp. 2015–2024.
KDD-2016-ZhaiCZZ #learning #named #online: DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks (SZ, KhC, RZ, Z(Z), pp. 1295–1304.
KDD-2016-ZhangT #named #performance: FINAL: Fast Attributed Network Alignment (SZ, HT), pp. 1345–1354.
KDD-2016-ZhangXKZ #evolution #named: NetCycle: Collective Evolution Inference in Heterogeneous Information Networks (YZ, YX, XK, YZ), pp. 1365–1374.
PLDI-2016-El-HassanyMBVV #analysis #concurrent #named: SDNRacer: concurrency analysis for software-defined networks (AEH, JM, PB, LV, MTV), pp. 402–415.
PLDI-2016-McClurgHFC #programming: Event-driven network programming (JM, HH, NF, PC), pp. 369–385.
PLDI-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.
POPL-2016-PlotkinBLRV #scalability #symmetry #using #verification: Scaling network verification using symmetry and surgery (GDP, NB, NPL, AR, GV), pp. 69–83.
ASE-2016-AbdessalemNBS #multi #testing #using: Testing advanced driver assistance systems using multi-objective search and neural networks (RBA, SN, LCB, TS), pp. 63–74.
ASE-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.
FSE-2016-Alexandru #synthesis #using: Guided code synthesis using deep neural networks (CVA), pp. 1068–1070.
CASE-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.
CASE-2016-LiuT #distributed #hybrid #multi #using: Distributed vision network for multiple target tracking using a dynamic hybrid consensus filter (GL, GT), pp. 805–808.
CASE-2016-ZouFP #estimation #metric #using: Psychoacoustic impacts estimation in manufacturing based on accelerometer measurement using artificial neural networks (MZ, LF, JP), pp. 1203–1208.
ESOP-2016-BresGH #algebra #process: A Timed Process Algebra for Wireless Networks with an Application in Routing - (Extended Abstract) (EB, RJvG, PH), pp. 95–122.
ICTSS-2016-SuzukiPKT #analysis #behaviour #visualisation: Distribution Visualization for User Behavior Analysis on LTE Network (MS, QP, TK, MT), pp. 249–255.
CBSE-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.
ECSA-2015-WangC #architecture #performance #social: A Specialised Social Network Software Architecture for Efficient Household Water Use Management (ZW, AC), pp. 146–153.
DRR-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).
HT-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.
HT-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.
HT-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.
HT-2015-KershawRS #online #social: Language Innovation and Change in On-line Social Networks (DK, MR, PS), pp. 311–314.
SIGMOD-2015-ErlingALCGPPB #benchmark #interactive #metric #social: The LDBC Social Network Benchmark: Interactive Workload (OE, AA, JLLP, HC, AG, APP, MDP, PAB), pp. 619–630.
SIGMOD-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.
SIGMOD-2015-JiangFW #keyword #scalability: Exact Top-k Nearest Keyword Search in Large Networks (MJ, AWCF, RCWW), pp. 393–404.
SIGMOD-2015-LiBCGM #named #towards: GetReal: Towards Realistic Selection of Influence Maximization Strategies in Competitive Networks (HL, SSB, JC, YG, JM), pp. 1525–1537.
SIGMOD-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.
SIGMOD-2015-WangLYXZ #approach #performance: Efficient Route Planning on Public Transportation Networks: A Labelling Approach (SW, WL, YY, XX, SZ), pp. 967–982.
VLDB-2015-LiQYM #community #scalability: Influential Community Search in Large Networks (RHL, LQ, JXY, RM), pp. 509–520.
VLDB-2015-NaziZT0D #online #performance #social: Walk, Not Wait: Faster Sampling Over Online Social Networks (AN, ZZ, ST, NZ, GD), pp. 678–689.
VLDB-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.
VLDB-2015-YuM #performance #scalability: Efficient Partial-Pairs SimRank Search for Large Networks (WY, JAM), pp. 569–580.
VLDB-2015-Zhou0D #online #performance #social: Leveraging History for Faster Sampling of Online Social Networks (ZZ, NZ, GD), pp. 1034–1045.
EDM-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.
EDM-2015-Eagle #interactive: Estimating the Local Size and Coverage of Interaction Network Regions (ME), pp. 671–673.
EDM-2015-EagleHB #estimation #interactive #predict #problem: Interaction Network Estimation: Predicting Problem-Solving Diversity in Interactive Environments (ME, AH, TB), pp. 342–349.
EDM-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.
EDM-2015-JiangZLL #analysis #question #what: Influence Analysis by Heterogeneous Network in MOOC Forums: What can We Discover? (ZJ, YZ, CL, XL), pp. 242–249.
SIGITE-2015-ChouH #research: Theoretical Research Project vs. Hands-on Project in a Network Management Course (TSC, EH), pp. 145–148.
SIGITE-2015-FeasterZH: Serious Toys: Introducing Sensors and Sensor Networks in Pre-collegiate Classrooms (YF, JZ, JOH), pp. 3–8.
SIGITE-2015-StrongGS #performance: Work in Progress: Improving the Performance of the Radial Basis Function Network (AS, TG, LS), p. 103.
ICALP-v2-2015-AminofRZS #liveness: Liveness of Parameterized Timed Networks (BA, SR, FZ, FS), pp. 375–387.
ICALP-v2-2015-AvinLNP: Core Size and Densification in Preferential Attachment Networks (CA, ZL, YN, DP), pp. 492–503.
ICALP-v2-2015-BringmannFHRS: Ultra-Fast Load Balancing on Scale-Free Networks (KB, TF, MH, RR, TS), pp. 516–527.
ICALP-v2-2015-Charron-BostFN #algorithm #approximate: Approximate Consensus in Highly Dynamic Networks: The Role of Averaging Algorithms (BCB, MF, TN), pp. 528–539.
ICALP-v2-2015-KarbasiLS #normalisation: Normalization Phenomena in Asynchronous Networks (AK, JL, AS), pp. 688–700.
LATA-2015-CodishCS #game studies #sorting: Sorting Networks: The End Game (MC, LCF, PSK), pp. 664–675.
LATA-2015-LiH #algebra #automaton #on the: On Observability of Automata Networks via Computational Algebra (RL, YH), pp. 249–262.
FM-2015-NelsonFK #difference #program analysis: Static Differential Program Analysis for Software-Defined Networks (TN, ADF, SK), pp. 395–413.
RTA-2015-Hellstrom #algebra: Network Rewriting II: Bi- and Hopf Algebras (LH), pp. 194–208.
CHI-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-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-PLAY-2015-TondelloWSLKN #game studies: CHI PLAYGUE: A Networking Game of Emergent Sociality (GFT, RRW, SNS, AL, RK, LEN), pp. 791–794.
CIG-2015-Miikkulainen #evolution #tutorial: Tutorial III: Evolving neural networks (RM), p. 22.
CIG-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.
DiGRA-2015-PaavilainenKKA #case study #experience #game studies #social: Exploring Playful Experiences in Social Network Games (JP, EK, HK, KA).
FDG-2015-Vrajitoru #game studies: A Pattern-Based Bayesian Network for the Game of Nine Men's Morris (DV).
GaM-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.
CSCW-2015-Borge-Holthoefer #twitter: Content and Network Dynamics Behind Egyptian Political Polarization on Twitter (JBH, WM, KD, IW), pp. 700–711.
CSCW-2015-Ferro #social: The Importance of Publicly Available Social Networking Sites (SNSs) to Entrepreneurs (TF), pp. 917–928.
CSCW-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.
CSCW-2015-IkkalaL: Monetizing Network Hospitality: Hospitality and Sociability in the Context of Airbnb (TI, AL), pp. 1033–1044.
CSCW-2015-IslamP #social: Engagement and Well-being on Social Network Sites (AKMNI, SP), pp. 375–382.
CSCW-2015-Medhi-ThiesFGOC #named #social: KrishiPustak: A Social Networking System for Low-Literate Farmers (IMT, PF, NG, JO, EC), pp. 1670–1681.
CSCW-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.
CSCW-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.
CSCW-2015-WisniewskiIKP #privacy #social: Give Social Network Users the Privacy They Want (PJW, AKMNI, BPK, SP), pp. 1427–1441.
DUXU-DD-2015-Frankjaer #smarttech #social: Soft Computation in the Public Sphere: Enhancing Social Dynamics with Wearable Networks (TRF), pp. 447–457.
DUXU-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.
DUXU-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.
HCI-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.
HCI-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.
HIMI-IKD-2015-NoseLBK #approach: Centralized Approach for a Unified Wireless Network Access (JDN, JL, CB, AK), pp. 547–559.
LCT-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.
SCSM-2015-AhnL #social: An Analytic Study on Private SNS for Bonding Social Networking (HA, SL), pp. 107–117.
SCSM-2015-Kanawati #community #detection: Ensemble Selection for Community Detection in Complex Networks (RK), pp. 138–147.
SCSM-2015-KastratiIYD #analysis #online #social #using: Analysis of Online Social Networks Posts to Investigate Suspects Using SEMCON (ZK, ASI, SYY, FD), pp. 148–157.
SCSM-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.
SCSM-2015-VillelaXP #collaboration #identification: Identifying Collaboration Strategies in Scientific Collaboration Networks (MLBV, SX, ROP), pp. 253–264.
CAiSE-2015-SenderovichWGMK #process #validation: Discovery and Validation of Queueing Networks in Scheduled Processes (AS, MW, AG, AM, SK, CAB), pp. 417–433.
ICEIS-v1-2015-CerqueiraOG #community #framework #scalability #social: A Framework for Analysing Dynamic Communities in Large-scale Social Networks (VC, MDBO, JG), pp. 235–242.
ICEIS-v1-2015-CoelhoC: Radial Basis Function Neural Network Receiver for Wireless Channels (PHGC, FMC), pp. 658–663.
ICEIS-v1-2015-SarmentoCG #streaming #using: Streaming Networks Sampling using top-K Networks (RS, MC, JG), pp. 228–234.
ICEIS-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.
ICEIS-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.
ICEIS-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.
ICEIS-v2-2015-PieroniP #configuration management #domain-specific language: A DSL for Configuration Management of Integrated Network Management System (RP, RADP), pp. 355–364.
ICEIS-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.
ICEIS-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.
ICEIS-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.
ICEIS-v3-2015-RosaZ #mobile #privacy #social: Location-sharing Model in Mobile Social Networks with Privacy Guarantee (TAR, SDZ), pp. 44–54.
CIKM-2015-AnwarLV0 #estimation #named: RoadRank: Traffic Diffusion and Influence Estimation in Dynamic Urban Road Networks (TA, CL, HLV, MSI0), pp. 1671–1674.
CIKM-2015-DingSGHYH #predict #sentiment #video: Video Popularity Prediction by Sentiment Propagation via Implicit Network (WD, YS, LG, XH, RY, TH), pp. 1621–1630.
CIKM-2015-GuoWWT #social #topic: Social-Relational Topic Model for Social Networks (WG, SW, LW0, TT), pp. 1731–1734.
CIKM-2015-HeLHTD #social: Extracting Interest Tags for Non-famous Users in Social Network (WH, HL, JH0, ST, XD0), pp. 861–870.
CIKM-2015-HeWJ #canonical #correlation #documentation #topic: Discovering Canonical Correlations between Topical and Topological Information in Document Networks (YH0, CW0, CJ), pp. 1281–1290.
CIKM-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.
CIKM-2015-KangGWM #algorithm #clustering #scalability: Scalable Clustering Algorithm via a Triangle Folding Processing for Complex Networks (YK, XG, WW0, DM), pp. 33–42.
CIKM-2015-LiP #multi #named #privacy: ReverseCloak: Protecting Multi-level Location Privacy over Road Networks (CL0, BP), pp. 673–682.
CIKM-2015-MaoLF #approach #recognition #transaction: Fraud Transaction Recognition: A Money Flow Network Approach (RM, ZL, JF), pp. 1871–1874.
CIKM-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.
CIKM-2015-RezvaniLXL #identification #scalability #social: Identifying Top-k Structural Hole Spanners in Large-Scale Social Networks (MR, WL, WX, CL), pp. 263–272.
CIKM-2015-ShiZLYYW #personalisation #recommendation #semantics: Semantic Path based Personalized Recommendation on Weighted Heterogeneous Information Networks (CS, ZZ, PL, PSY, YY, BW0), pp. 453–462.
CIKM-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.
CIKM-2015-SongHL #mining #social: Mining Brokers in Dynamic Social Networks (CS, WH, MLL), pp. 523–532.
CIKM-2015-TuarobTSR #modelling #social #using: Modeling Individual-Level Infection Dynamics Using Social Network Information (ST, CST, MS, NR), pp. 1501–1510.
CIKM-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.
CIKM-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.
CIKM-2015-YeL #constraints #logic #markov #multi: Structural Constraints for Multipartite Entity Resolution with Markov Logic Network (TY, HWL), pp. 1691–1694.
CIKM-2015-ZhaoZK #analysis #game studies #recommendation #social: Exploiting Game Theoretic Analysis for Link Recommendation in Social Networks (TZ, HVZ, IK), pp. 851–860.
CIKM-2015-ZhouCLXZL #social: Location-Based Influence Maximization in Social Networks (TZ, JC, BL0, SX, ZZ, JL), pp. 1211–1220.
ECIR-2015-LiTWLR #automation #quality #wiki: Automatically Assessing Wikipedia Article Quality by Exploiting Article-Editor Networks (XL, JT, TW, ZL, MdR), pp. 574–580.
ECIR-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.
ICML-2015-AnBB #how #linear #question: How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances? (SA, FB, MB), pp. 514–523.
ICML-2015-BachmanP #collaboration #generative #probability: Variational Generative Stochastic Networks with Collaborative Shaping (PB, DP), pp. 1964–1972.
ICML-2015-BlundellCKW #nondeterminism: Weight Uncertainty in Neural Network (CB, JC, KK, DW), pp. 1613–1622.
ICML-2015-ChenWTWC: Compressing Neural Networks with the Hashing Trick (WC, JTW, ST, KQW, YC), pp. 2285–2294.
ICML-2015-ChungGCB #feedback: Gated Feedback Recurrent Neural Networks (JC, ÇG, KC, YB), pp. 2067–2075.
ICML-2015-ClarkS #game studies: Training Deep Convolutional Neural Networks to Play Go (CC, AJS), pp. 1766–1774.
ICML-2015-FouldsKG #framework #modelling #probability #programming #topic: Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models (JRF, SHK, LG), pp. 777–786.
ICML-2015-GregorDGRW #generative #image #named: DRAW: A Recurrent Neural Network For Image Generation (KG, ID, AG, DJR, DW), pp. 1462–1471.
ICML-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.
ICML-2015-Hernandez-Lobato15b #learning #probability #scalability: Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks (JMHL, RA), pp. 1861–1869.
ICML-2015-HongYKH #learning #online: Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network (SH, TY, SK, BH), pp. 597–606.
ICML-2015-IoffeS #normalisation: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (SI, CS), pp. 448–456.
ICML-2015-JozefowiczZS #architecture #empirical: An Empirical Exploration of Recurrent Network Architectures (RJ, WZ, IS), pp. 2342–2350.
ICML-2015-LiSZ #generative: Generative Moment Matching Networks (YL, KS, RSZ), pp. 1718–1727.
ICML-2015-LongC0J #adaptation #learning: Learning Transferable Features with Deep Adaptation Networks (ML, YC, JW, MJ), pp. 97–105.
ICML-2015-MartensG #approximate #optimisation: Optimizing Neural Networks with Kronecker-factored Approximate Curvature (JM, RBG), pp. 2408–2417.
ICML-2015-SnoekRSKSSPPA #optimisation #scalability #using: Scalable Bayesian Optimization Using Deep Neural Networks (JS, OR, KS, RK, NS, NS, MMAP, P, RPA), pp. 2171–2180.
ICML-2015-SunWKM #geometry: Information Geometry and Minimum Description Length Networks (KS, JW, AK, SMM), pp. 49–58.
ICML-2015-TangSX #learning: Learning Scale-Free Networks by Dynamic Node Specific Degree Prior (QT, SS, JX), pp. 2247–2255.
ICML-2015-ZhaoMP #on the: On the Relationship between Sum-Product Networks and Bayesian Networks (HZ, MM, PP), pp. 116–124.
KDD-2015-0002PGM #on the: On the Formation of Circles in Co-authorship Networks (TC, SP, PG, AM), pp. 109–118.
KDD-2015-AgrawalGP #question #social #web: Whither Social Networks for Web Search? (RA, BG, EEP), pp. 1661–1670.
KDD-2015-ChangHTQAH #architecture: Heterogeneous Network Embedding via Deep Architectures (SC, WH, JT, GJQ, CCA, TSH), pp. 119–128.
KDD-2015-ChierichettiEKL #algorithm #performance #social: Efficient Algorithms for Public-Private Social Networks (FC, AE, RK, SL, VSM), pp. 139–148.
KDD-2015-DongZTCW #named #predict: CoupledLP: Link Prediction in Coupled Networks (YD, JZ, JT, NVC, BW), pp. 199–208.
KDD-2015-FakhraeiFSG #detection #evolution #multi #social: Collective Spammer Detection in Evolving Multi-Relational Social Networks (SF, JRF, MVSS, LG), pp. 1769–1778.
KDD-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.
KDD-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.
KDD-2015-HallacLB #clustering #graph #optimisation #scalability: Network Lasso: Clustering and Optimization in Large Graphs (DH, JL, SB), pp. 387–396.
KDD-2015-HanT #community #probability #social: Probabilistic Community and Role Model for Social Networks (YH, JT), pp. 407–416.
KDD-2015-JiangZT #capacity #constraints #social: Reciprocity in Social Networks with Capacity Constraints (BJ, ZLZ, DT), pp. 457–466.
KDD-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.
KDD-2015-LucierOS #distributed #scalability: Influence at Scale: Distributed Computation of Complex Contagion in Networks (BL, JO, YS), pp. 735–744.
KDD-2015-McAuleyPL: Inferring Networks of Substitutable and Complementary Products (JJM, RP, JL), pp. 785–794.
KDD-2015-MitzenmacherPPT #clique #detection #scalability: Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling (MM, JP, RP, CET, SCX), pp. 815–824.
KDD-2015-OhsakaMK #evolution #performance #rank: Efficient PageRank Tracking in Evolving Networks (NO, TM, KiK), pp. 875–884.
KDD-2015-RaptiSTT #multi: Virus Propagation in Multiple Profile Networks (AR, SS, KT, GT), pp. 975–984.
KDD-2015-RayanaA #detection #metadata #overview: Collective Opinion Spam Detection: Bridging Review Networks and Metadata (SR, LA), pp. 985–994.
KDD-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.
KDD-2015-SongMT #performance #recommendation: Efficient Latent Link Recommendation in Signed Networks (DS, DAM, DT), pp. 1105–1114.
KDD-2015-SpasojevicLRB #social: When-To-Post on Social Networks (NS, ZL, AR, PB), pp. 2127–2136.
KDD-2015-TangQM #named #predict #scalability: PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks (JT, MQ, QM), pp. 1165–1174.
KDD-2015-WangSERZH #clustering #documentation: Incorporating World Knowledge to Document Clustering via Heterogeneous Information Networks (CW, YS, AEK, DR, MZ, JH), pp. 1215–1224.
KDD-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.
KDD-2015-ZhangTMTJL #named #performance #scalability #similarity: Panther: Fast Top-k Similarity Search on Large Networks (JZ, JT, CM, HT, YJ, JL), pp. 1445–1454.
KDD-2015-ZhangTYPY #consistency #named #social: COSNET: Connecting Heterogeneous Social Networks with Local and Global Consistency (YZ, JT, ZY, JP, PSY), pp. 1485–1494.
KDD-2015-ZhuPCZZ #modelling #social: Modeling User Mobility for Location Promotion in Location-based Social Networks (WYZ, WCP, LJC, KZ, XZ), pp. 1573–1582.
RecSys-2015-ChaneyBE #personalisation #probability #recommendation #social #using: A Probabilistic Model for Using Social Networks in Personalized Item Recommendation (AJBC, DMB, TER), pp. 43–50.
RecSys-2015-MacedoMS #recommendation #social: Context-Aware Event Recommendation in Event-based Social Networks (AQdM, LBM, RLTS), pp. 123–130.
RecSys-2015-Salehi-AbariB #recommendation #social: Preference-oriented Social Networks: Group Recommendation and Inference (ASA, CB), pp. 35–42.
RecSys-2015-SousaDBM #analysis #named #recommendation: CNARe: Co-authorship Networks Analysis and Recommendations (GAdS, MAD, MAB, MMM), pp. 329–330.
SEKE-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.
SEKE-2015-GokhaleE #analysis #social: Social Analysis of the SEKE Co-Author Network (SSG, REK), pp. 237–243.
SEKE-2015-NetoSZD #using: Using implications from FCA to represent a two mode network data (SMN, MAJS, LEZ, SMD), pp. 256–259.
SIGIR-2015-HsiehLY #social: I See You: Person-of-Interest Search in Social Networks (HPH, CTL, RY), pp. 839–842.
SIGIR-2015-SeverynM #learning #rank: Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks (AS, AM), pp. 373–382.
SIGIR-2015-SeverynM15a #analysis #sentiment #twitter: Twitter Sentiment Analysis with Deep Convolutional Neural Networks (AS, AM), pp. 959–962.
SIGIR-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.
MoDELS-2015-LuddeckeSSS #modelling #using: Modeling user intentions for in-car infotainment systems using Bayesian networks (DL, CS, JS, IS), pp. 378–385.
LOPSTR-2015-CodishCNS #library #sorting: Applying Sorting Networks to Synthesize Optimized Sorting Libraries (MC, LCF, MN, PSK), pp. 127–142.
LOPSTR-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.
PLDI-2015-McClurgHCF #performance #synthesis: Efficient synthesis of network updates (JM, HH, PC, NF), pp. 196–207.
POPL-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.
PPDP-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-FSE-2015-ArcuriFG #automation #generative #testing: Generating TCP/UDP network data for automated unit test generation (AA, GF, JPG), pp. 155–165.
ICSE-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.
SAC-2015-ArbizaBSGT #internet #middleware #refactoring: Refactoring internet of things middleware through software-defined network (LMRA, LMB, CRPdS, LZG, LMRT), pp. 640–645.
SAC-2015-BoukAK #challenge #overview #research: Vehicular content centric network (VCCN): a survey and research challenges (SHB, SHA, DK), pp. 695–700.
SAC-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.
SAC-2015-ChoobdarRS: Discovering weighted motifs in gene co-expression networks (SC, PMPR, FMAS), pp. 10–17.
SAC-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.
SAC-2015-ErnstKR #evaluation #performance: Performance evaluation of heterogeneous wireless networks considering competing objectives and viewpoints (JBE, SCK, JJPCR), pp. 680–687.
SAC-2015-FernandesPCRP #detection #metaheuristic #statistics: Statistical, forecasting and metaheuristic techniques for network anomaly detection (GF, EHMP, LFC, JJPCR, MLPJ), pp. 701–707.
SAC-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.
SAC-2015-GkorouPE #distributed #trust: Trust-based collection of information in distributed reputation networks (DG, JAP, DHJE), pp. 2312–2319.
SAC-2015-GomesB #integration #mobile: Feasibility of information-centric networking integration into LTE mobile networks (AG, TB), pp. 627–633.
SAC-2015-GotoT #communication #detection #visual notation: Anomalous network communication detection system by visual pattern on a client computer (HG, TT), pp. 1263–1269.
SAC-2015-Grossl #modelling: Modeling dependable systems with continuous time Bayesian networks (MG), pp. 436–441.
SAC-2015-IqbalAB #design #framework #scheduling: Designing network servers within a hierarchical scheduling framework (ZI, LA, MB), pp. 653–658.
SAC-2015-JamhourPPSB: Interference aware channel assignment for structured wireless sensor networks (EJ, MEP, MCP, RDS, GGdOB), pp. 716–719.
SAC-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.
SAC-2015-KatsalisSPKT: Content placement in heterogeneous end-to-end virtual networks (KK, VS, TP, TK, LT), pp. 602–608.
SAC-2015-Khan #multi: Multi-criteria based vertical handover decision in heterogeneous wireless network (MK), pp. 720–721.
SAC-2015-LagoMM #estimation #power management: High speed network impacts and power consumption estimation for cloud data centers (DGdL, ERMM, DM), pp. 615–620.
SAC-2015-LevoratoDFF #algorithm #social: An ILS algorithm to evaluate structural balance in signed social networks (ML, LMdAD, YF, RMVdF), pp. 1117–1122.
SAC-2015-LiZL #mobile #personalisation #social: Integrating mobile sensing and social network for personalized health-care application (HL, QZ, KL), pp. 527–534.
SAC-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.
SAC-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.
SAC-2015-MarquesRPSM #coordination #named: NVL: a coordination language for unmanned vehicle networks (ERBM, MR, JP, JBS, FM), pp. 331–334.
SAC-2015-MatlCD #effectiveness: Effective manycast messaging for Kademlia network (LM, TC, MJD), pp. 646–652.
SAC-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.
SAC-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.
SAC-2015-TroisMBF: From software defined network to network defined for software (CT, MM, LCEDB, MDDF), pp. 665–668.
SAC-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.
SAC-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.
ASPLOS-2015-TanQCAP #named #using: DIABLO: A Warehouse-Scale Computer Network Simulator using FPGAs (ZT, ZQ, XC, KA, DAP), pp. 207–221.
CASE-2015-ButtersGS #detection: Detecting and reducing redundancy in alarm networks (TDB, SG, JLS), pp. 1224–1229.
CASE-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.
CASE-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.
CASE-2015-DobslawGZ #challenge #industrial #using: Challenges for the use of data aggregation in industrial Wireless Sensor Networks (FD, MG, TZ), pp. 138–144.
CASE-2015-JiYA #automation #mobile #re-engineering: Automatic calibration and trajectory reconstruction of mobile robot in camera sensor network (YJ, AY, HA), pp. 206–211.
CASE-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.
CASE-2015-KanY #image #modelling #monitoring: Network models for monitoring high-dimensional image profiles (CK, HY), pp. 1078–1083.
CASE-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.
CASE-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.
CASE-2015-YuAGB #industrial #metric: Realization and measurements of industrial wireless sensor and actuator networks (KY, JÅ, MG, MB), pp. 131–137.
DAC-2015-CavigelliMB #embedded #realtime: Accelerating real-time embedded scene labeling with convolutional networks (LC, MM, LB), p. 6.
DAC-2015-CongGHRY #architecture: On-chip interconnection network for accelerator-rich architectures (JC, MG, YH, GR, BY), p. 6.
DAC-2015-LiuKDK #data access #reduction: Network footprint reduction through data access and computation placement in NoC-based manycores (JL, JK, WD, MTK), p. 6.
DAC-2015-NishimiyaSS #evaluation #functional #interface #mockup #modelling: Evaluation of functional mock-up interface for vehicle power network modeling (KN, TS, SS), p. 6.
DAC-2015-ShreejithF #embedded #generative #security: Security aware network controllers for next generation automotive embedded systems (SS, SAF), p. 6.
DAC-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.
DATE-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.
DATE-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.
DATE-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.
DATE-2015-MirhosseiniSFMS #energy: An energy-efficient virtual channel power-gating mechanism for on-chip networks (AM, MS, AF, MM, HSA), pp. 1527–1532.
DATE-2015-MundhenkSLFC #authentication #lightweight: Lightweight authentication for secure automotive networks (PM, SS, ML, SAF, SC), pp. 285–288.
DATE-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.
DATE-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.
DATE-2015-ThangamuthuCCL #analysis: Analysis of ethernet-switch traffic shapers for in-vehicle networking applications (ST, NC, PJLC, JJL), pp. 55–60.
DATE-2015-ZhangWTYX #approximate #framework #named: ApproxANN: an approximate computing framework for artificial neural network (QZ, TW, YT, FY, QX), pp. 701–706.
HPCA-2015-ChrysosMRBV #named: SCOC: High-radix switches made of bufferless clos networks (NC, CM, MR, CB, BV), pp. 402–414.
HPCA-2015-FujiwaraKOMC: Augmenting low-latency HPC network with free-space optical links (IF, MK, TO, HM, HC), pp. 390–401.
HPCA-2015-WonKKJPS #scalability: Overcoming far-end congestion in large-scale networks (JW, GK, JK, TJ, MP, SS), pp. 415–427.
HPDC-2015-PokeH #named #replication #state machine: DARE: High-Performance State Machine Replication on RDMA Networks (MP, TH), pp. 107–118.
PDP-2015-BlaskiewiczZBD #gpu #parallel: An Application of GPU Parallel Computing to Power Flow Calculation in HVDC Networks (PB, MZ, PB, PD), pp. 635–641.
PDP-2015-FedorchenkoKC #analysis #database #design #security: Design of Integrated Vulnerabilities Database for Computer Networks Security Analysis (AF, IVK, AC), pp. 559–566.
PDP-2015-HenrioMM #named #process: pNets: An Expressive Model for Parameterised Networks of Processes (LH, EM, MM), pp. 492–496.
PDP-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.
PDP-2015-NgyenJDHDPT #framework #named: FIST: A Framework to Interleave Spiking Neural Networks on CGRAs (TN, SMAHJ, MD, AH, SD, JP, HT), pp. 751–758.
STOC-2015-Czumaj #permutation #random #using: Random Permutations using Switching Networks (AC), pp. 703–712.
TACAS-2015-GiacobbeGGHPP #model checking: Model Checking Gene Regulatory Networks (MG, CCG, AG, TAH, TP, TP), pp. 469–483.
TACAS-2015-NamjoshiT #analysis #process: Analysis of Dynamic Process Networks (KSN, RJT), pp. 164–178.
CAV-2015-AbateBCK #adaptation #analysis #markov: Adaptive Aggregation of Markov Chains: Quantitative Analysis of Chemical Reaction Networks (AA, LB, MC, MZK), pp. 195–213.
CAV-2015-FisherKPW #execution: Synthesising Executable Gene Regulatory Networks from Single-Cell Gene Expression Data (JF, ASK, NP, SW), pp. 544–560.
ICTSS-2015-KitaharaNSFA #capacity #evaluation: A Practical Evaluation Method of Network Traffic Load for Capacity Planning (TK, SN, MS, NF, SA), pp. 263–268.
ECSA-2014-BerardinelliMP #analysis #design #performance: fUML-Driven Design and Performance Analysis of Software Agents for Wireless Sensor Network (LB, ADM, SP), pp. 324–339.
HT-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.
HT-2014-ChelaruHNS #communication: Recognizing skill networks and their specific communication and connection practices (SC, EH, KDN, PS), pp. 13–23.
HT-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.
HT-2014-WenLTP #twitter: Twitter in academic conferences: usage, networking and participation over time (XW, YRL, CT, DP), pp. 285–290.
HT-2014-ZhangBR #analysis #empirical #social #social media: Empirical analysis of implicit brand networks on social media (KZ, SB, SR), pp. 190–199.
JCDL-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.
JCDL-2014-BarkerBM #library: Vector-Borne Disease Network digital library (MB, DB, NM), pp. 435–436.
JCDL-2014-CostaQW #repository #research: Research networks in data repositories (MRC, JQ, JW), pp. 403–406.
JCDL-2014-LarsonPT #named #social #towards: SNAC: The Social Networks and Archival Context project — Towards an archival authority cooperative (RRL, DP, AT), pp. 427–428.
JCDL-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.
PODS-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.
SIGMOD-2014-ChenLWXML #algorithm #performance #query: Efficient algorithms for optimal location queries in road networks (ZC, YL, RCWW, JX, GM, CL), pp. 123–134.
SIGMOD-2014-Dev #algorithm #community #detection #interactive #online #social: A user interaction based community detection algorithm for online social networks (HD), pp. 1607–1608.
SIGMOD-2014-FengCBM #online #social: In search of influential event organizers in online social networks (KF, GC, SSB, SM), pp. 63–74.
SIGMOD-2014-LevinK #pipes and filters #social #using: Stratified-sampling over social networks using mapreduce (RL, YK), pp. 863–874.
SIGMOD-2014-PolychroniouSR #distributed: Track join: distributed joins with minimal network traffic (OP, RS, KAR), pp. 1483–1494.
SIGMOD-2014-ShenHW #probability #web: A probabilistic model for linking named entities in web text with heterogeneous information networks (WS, JH, JW), pp. 1199–1210.
SIGMOD-2014-ShiMWC #clustering: Density-based place clustering in geo-social networks (JS, NM, DW, DWC), pp. 99–110.
SIGMOD-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.
SIGMOD-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.
SIGMOD-2014-ZhangCPSX #named: PrivBayes: private data release via bayesian networks (JZ, GC, CMP, DS, XX), pp. 1423–1434.
VLDB-2014-HuangBJW #realtime #scalability: Large Scale Real-time Ridesharing with Service Guarantee on Road Networks (YH, FB, RJ, XSW), pp. 2017–2028.
VLDB-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.
VLDB-2014-KongLH #named #social #social media: SPOT: Locating Social Media Users Based on Social Network Context (LK, ZL, YH), pp. 1681–1684.
VLDB-2014-KorulaL #algorithm #performance #social: An efficient reconciliation algorithm for social networks (NK, SL), pp. 377–388.
VLDB-2014-SongSZZ #framework #named #novel: PRESS: A Novel Framework of Trajectory Compression in Road Networks (RS, WS, BZ, YZ), pp. 661–672.
VLDB-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.
SIGITE-2014-SchmidtHM #education #physics: A virtualized testbed with physical outlets for hands-on computer networking education (MS, FH, MM), pp. 3–8.
SIGITE-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.
SIGITE-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.
SIGITE-2014-WangBM #design #education #lessons learnt: Teaching a networking class for freshmen: course design and lessons learned (YW, TB, MM), pp. 9–14.
ICPC-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.
ICALP-v1-2014-KontogiannisZ #distance: Distance Oracles for Time-Dependent Networks (SCK, CDZ), pp. 713–725.
ICALP-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.
ICALP-v2-2014-AschnerK #bound #constraints #modelling: Bounded-Angle Spanning Tree: Modeling Networks with Angular Constraints (RA, MJK), pp. 387–398.
ICALP-v2-2014-AvinBLP #axiom #design #distributed: Distributed Computing on Core-Periphery Networks: Axiom-Based Design (CA, MB, ZL, DP), pp. 399–410.
ICALP-v2-2014-ChalopinDLP #fault tolerance: Fault-Tolerant Rendezvous in Networks (JC, YD, AL, AP), pp. 411–422.
ICALP-v2-2014-DamsHK #learning: Jamming-Resistant Learning in Wireless Networks (JD, MH, TK), pp. 447–458.
ICALP-v2-2014-EmekSW: Computability in Anonymous Networks: Revocable vs. Irrecovable Outputs (YE, JS, RW), pp. 183–195.
LATA-2014-ArroyoCMP: Networks of Polarized Evolutionary Processors Are Computationally Complete (FA, SGC, VM, SP), pp. 101–112.
LATA-2014-BreveglieriCM #parsing: Shift-Reduce Parsers for Transition Networks (LB, SCR, AM), pp. 222–235.
LATA-2014-BundalaZ #sorting: Optimal Sorting Networks (DB, JZ), pp. 236–247.
LATA-2014-Martos-SalgadoR #petri net: Expressiveness of Dynamic Networks of Timed Petri Nets (MMS, FRV), pp. 516–527.
FM-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.
SEFM-2014-PardoS #framework #policy #privacy #social: A Formal Privacy Policy Framework for Social Networks (RP, GS), pp. 378–392.
CIG-2014-AsensioDC #evolution: Evolving Artificial Neural Networks applied to generate virtual characters (JMLA, JPD, PC0), pp. 1–5.
CIG-2014-SzubertJ #difference #game studies #learning: Temporal difference learning of N-tuple networks for the game 2048 (MGS, WJ), pp. 1–8.
GT-VMT-2014-HusseinHDS #adaptation #modelling: Modelling Adaptive Networks: The Case of the Petrified Voters (MH, RH, VD, PS).
CHI-2014-BachPF #matrix #visualisation: Visualizing dynamic networks with matrix cubes (BB, EP, JDF), pp. 877–886.
CHI-2014-BurkeK #facebook #social: Growing closer on facebook: changes in tie strength through social network site use (MB, REK), pp. 4187–4196.
CHI-2014-DaviesLCEFKS #personalisation #pervasive #privacy: Personalisation and privacy in future pervasive display networks (ND, ML, SC, IE, AF, TK, BS), pp. 2357–2366.
CHI-2014-ForlinesMGB #crowdsourcing #predict #social: Crowdsourcing the future: predictions made with a social network (CF, SM, LG, RB), pp. 3655–3664.
CHI-2014-Hale #multi #twitter: Global connectivity and multilinguals in the Twitter network (SAH), pp. 833–842.
CHI-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.
CHI-2014-McGookinBC #social: Studying digital graffiti as a location-based social network (DKM, SAB, GC), pp. 3269–3278.
CHI-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.
CHI-2014-Smith-ClarkeMC #communication #mobile #using: Poverty on the cheap: estimating poverty maps using aggregated mobile communication networks (CSC, AJM, LC), pp. 511–520.
CHI-2014-WuA #online #social #visual notation: Visually impaired users on an online social network (SW, LAA), pp. 3133–3142.
CSCW-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.
CSCW-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.
CSCW-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.
CSCW-2014-LingelNB #online: City, self, network: transnational migrants and online identity work (JL, MN, DB), pp. 1502–1510.
CSCW-2014-MarlowD #design #development #social: From rookie to all-star: professional development in a graphic design social networking site (JM, LD), pp. 922–933.
CSCW-2014-Morris #social #women: Social networking site use by mothers of young children (MRM), pp. 1272–1282.
CSCW-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.
CSCW-2014-ZhangCG #evolution #social: Creepy but inevitable?: the evolution of social networking (HZ, MDC, JG), pp. 368–378.
DHM-2014-ZhangGBD #industrial: Application of Bayesian Networks in Consumer Service Industry and Healthcare (LZ, YG, BB, VGD), pp. 484–495.
DUXU-DI-2014-ArfaaW14a #social #usability: A Usability Study on Elder Adults Utilizing Social Networking Sites (JA, Y(W), pp. 50–61.
DUXU-DI-2014-FrankjaerG #hybrid #smarttech: Wearable Networks, Creating Hybrid Spaces with Soft Circuits (TRF, DG), pp. 435–445.
DUXU-ELAS-2014-Abdullah #monitoring #simulation: Simulation of Wireless Sensor Network for Flood Monitoring System (MA), pp. 255–264.
DUXU-ELAS-2014-EmilianoSFBP: Traffic Management in Rural Networks (RE, FS, LF, JB, AP), pp. 452–461.
HCI-AS-2014-Moallem: Home Networking: Smart but Complicated (AM), pp. 731–741.
HIMI-AS-2014-JiangLLC #identification #sequence: Neural Networks for Identifying Civil Pilot’s Operation Sequences (ZJ, QL, YL, BC), pp. 241–252.
HIMI-AS-2014-MaeshiroM #process: Polyhedron Network Model to Describe Creative Processes (TM, MM), pp. 535–545.
HIMI-DE-2014-MatsunagaY #documentation: Digital Document Network System for Organizing Individual Knowledge (KM, KY), pp. 396–403.
HIMI-DE-2014-PinheiroCM #social: Increasing Information Auditability for Social Network Users (AP, CC, CM), pp. 536–547.
HIMI-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.
LCT-TRE-2014-BaldiniKLT #social #trust: European Citizens and Their Trust in Social Networks (GB, IK, JL, MT), pp. 363–374.
LCT-TRE-2014-OliveiraM #learning #research: Digital Identity of Researchers and Their Personal Learning Network (NRO, LM), pp. 467–477.
SCSM-2014-AbdallaY #online #overview #process #social #using: A Review of Using Online Social Networks for Investigative Activities (AA, SYY), pp. 3–12.
SCSM-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.
SCSM-2014-AlvesMA #gamification #guidelines #mobile #social: Guidelines for the Gamification in Mobile Social Networks (FPA, CM, JCA), pp. 559–570.
SCSM-2014-FardounAC #education #representation #social #student: Representing Students Curriculum in Social Networks (HMF, AA, APC), pp. 48–58.
SCSM-2014-FawziS #gender #online #social: An Investigation into Gender Role Conformity in an Online Social Networking Environment (AF, AS), pp. 322–330.
SCSM-2014-Fu #microblog #social: Can Network Help Chinese Microblogs Diffuse? Analyzing 118 Networks of Reposts About Social Issues in China (KwF), pp. 331–341.
SCSM-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.
SCSM-2014-Kanawati #community #detection: Seed-Centric Approaches for Community Detection in Complex Networks (RK), pp. 197–208.
SCSM-2014-LeeLS #approach #social: A New Approach to Exploring Spatiotemporal Space in the Context of Social Network Services (JGL, KCL, DHS), pp. 221–228.
SCSM-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.
SCSM-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.
SCSM-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.
SCSM-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.
CAiSE-2014-BennacerJPQ #social: Matching User Profiles Across Social Networks (NB, CNJ, AP, GQ), pp. 424–438.
CAiSE-2014-StulpnagelOS #logic #markov #risk management: IT Risk Management with Markov Logic Networks (JvS, JO, JS), pp. 301–315.
CAiSE-2014-VergneS #community #markov #open source #using: Expert Finding Using Markov Networks in Open Source Communities (MV, AS), pp. 196–210.
ICEIS-v1-2014-CoelhoAABB #using: Router Nodes Positioning for Wireless Networks Using Artificial Immune Systems (PHGC, JLMdA, JFMdA, LFdAB, AVdB), pp. 415–421.
ICEIS-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.
ICEIS-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.
ICEIS-v2-2014-AlanneKN #development #enterprise: Networks of Pain in ERP Development (AA, TK, EN), pp. 257–266.
ICEIS-v2-2014-KaramtiTG #image #process #retrieval #using: Vectorization of Content-based Image Retrieval Process Using Neural Network (HK, MT, FG), pp. 435–439.
ICEIS-v2-2014-LiuDT #reliability: Auditing Data Reliability in International Logistics — An Application of Bayesian Networks (LL, HAMD, RT), pp. 707–712.
ICEIS-v2-2014-VieiraJF #analysis: A Risk Analysis Method for Selecting Service Providers in P2P Service Overlay Networks (RGV, OCAJ, AF), pp. 477–488.
CIKM-2014-DahimeneCM #named #recommendation #social: RecLand: A Recommender System for Social Networks (RD, CC, CdM), pp. 2063–2065.
CIKM-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.
CIKM-2014-GuiSHB #modelling #multi #topic: Modeling Topic Diffusion in Multi-Relational Bibliographic Information Networks (HG, YS, JH, GB), pp. 649–658.
CIKM-2014-JiaDGZ #analysis #community: Analysis on Community Variational Trend in Dynamic Networks (XJ, ND, JG, AZ), pp. 151–160.
CIKM-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.
CIKM-2014-LiuXD #mining #predict: Relationship Emergence Prediction in Heterogeneous Networks through Dynamic Frequent Subgraph Mining (YL, SX, LD), pp. 1649–1658.
CIKM-2014-LuciaF #classification #knowledge-based #named: EgoCentric: Ego Networks for Knowledge-based Short Text Classification (WL, EF), pp. 1079–1088.
CIKM-2014-LuSY #identification #social: Identifying Your Customers in Social Networks (CTL, HHS, PSY), pp. 391–400.
CIKM-2014-MahdabiC #mining #recommendation #retrieval: Query-Driven Mining of Citation Networks for Patent Citation Retrieval and Recommendation (PM, FC), pp. 1659–1668.
CIKM-2014-PfeifferNB #learning #probability #using: Active Exploration in Networks: Using Probabilistic Relationships for Learning and Inference (JJPI, JN, PNB), pp. 639–648.
CIKM-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.
CIKM-2014-RahmanH #strict #using: Sampling Triples from Restricted Networks using MCMC Strategy (MR, MAH), pp. 1519–1528.
CIKM-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.
CIKM-2014-ShenJ #information management #multi #online #social: Controllable Information Sharing for User Accounts Linkage across Multiple Online Social Networks (YS, HJ), pp. 381–390.
CIKM-2014-ShihKRCGSP #component #detection: Component Detection in Directed Networks (YKS, SK, YR, JC, AG, TS, SP), pp. 1729–1738.
CIKM-2014-ShiWLYW #clustering: Ranking-based Clustering on General Heterogeneous Information Networks by Network Projection (CS, RW, YL, PSY, BW), pp. 699–708.
CIKM-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.
CIKM-2014-WangWC #community #detection #named #social: CoDEM: An Ingenious Tool of Insight into Community Detection in Social Networks (MW, CW, JC), pp. 2006–2008.
CIKM-2014-YuX #interactive #learning #predict #scalability #social: Learning Interactions for Social Prediction in Large-scale Networks (XY, JX), pp. 161–170.
ECIR-2014-LiakosPS #locality #on the #social: On the Effect of Locality in Compressing Social Networks (PL, KP, MS), pp. 650–655.
ECIR-2014-LuoGWL #algorithm #classification #named #novel: HetPathMine: A Novel Transductive Classification Algorithm on Heterogeneous Information Networks (CL, RG, ZW, CL), pp. 210–221.
ECIR-2014-ZhangZWS #recommendation: Content + Attributes: A Latent Factor Model for Recommending Scientific Papers in Heterogeneous Academic Networks (CZ, XZ, KW, JS), pp. 39–50.
ICML-c1-2014-PinheiroC: Recurrent Convolutional Neural Networks for Scene Labeling (PHOP, RC), pp. 82–90.
ICML-c1-2014-RooshenasL #interactive #learning: Learning Sum-Product Networks with Direct and Indirect Variable Interactions (AR, DL), pp. 710–718.
ICML-c1-2014-ZhouT #generative #predict #probability: Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction (JZ, OGT), pp. 745–753.
ICML-c2-2014-AndoniPV0 #learning: Learning Polynomials with Neural Networks (AA, RP, GV, LZ), pp. 1908–1916.
ICML-c2-2014-AziziAG #composition #learning: Learning Modular Structures from Network Data and Node Variables (EA, EA, JEG), pp. 1440–1448.
ICML-c2-2014-BengioLAY #generative #probability: Deep Generative Stochastic Networks Trainable by Backprop (YB, EL, GA, JY), pp. 226–234.
ICML-c2-2014-CarlssonMRS #clustering #symmetry: Hierarchical Quasi-Clustering Methods for Asymmetric Networks (GEC, FM, AR, SS), pp. 352–360.
ICML-c2-2014-CelikLL #estimation #performance #reduction: Efficient Dimensionality Reduction for High-Dimensional Network Estimation (SC, BAL, SIL), pp. 1953–1961.
ICML-c2-2014-ChakrabartiFCM #multi #scalability: Joint Inference of Multiple Label Types in Large Networks (DC, SF, JC, SAM), pp. 874–882.
ICML-c2-2014-DaneshmandGSS #algorithm #complexity: Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm (HD, MGR, LS, BS), pp. 793–801.
ICML-c2-2014-DuLBS #information management #learning: Influence Function Learning in Information Diffusion Networks (ND, YL, MFB, LS), pp. 2016–2024.
ICML-c2-2014-GravesJ #recognition #speech #towards: Towards End-To-End Speech Recognition with Recurrent Neural Networks (AG, NJ), pp. 1764–1772.
ICML-c2-2014-GregorDMBW: Deep AutoRegressive Networks (KG, ID, AM, CB, DW), pp. 1242–1250.
ICML-c2-2014-LevineK #learning #optimisation #policy: Learning Complex Neural Network Policies with Trajectory Optimization (SL, VK), pp. 829–837.
ICML-c2-2014-LindermanA #process: Discovering Latent Network Structure in Point Process Data (SWL, RPA), pp. 1413–1421.
ICML-c2-2014-MnihG #learning: Neural Variational Inference and Learning in Belief Networks (AM, KG), pp. 1791–1799.
ICML-c2-2014-PandeyD #learning: Learning by Stretching Deep Networks (GP, AD), pp. 1719–1727.
ICML-c2-2014-SuGR #predict: Structured Prediction of Network Response (HS, AG, JR), pp. 442–450.
ICML-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.
ICPR-2014-AlvaroSB: Offline Features for Classifying Handwritten Math Symbols with Recurrent Neural Networks (FA, JAS, JMB), pp. 2944–2949.
ICPR-2014-ByeonLB #2d #classification #using: Texture Classification Using 2D LSTM Networks (WB, ML, TMB), pp. 1144–1149.
ICPR-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.
ICPR-2014-DongPHLDJ #classification #using: Vehicle Type Classification Using Unsupervised Convolutional Neural Network (ZD, MP, YH, TL, YD, YJ), pp. 172–177.
ICPR-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.
ICPR-2014-FuscoEM #data analysis #locality: Bad Data Analysis with Sparse Sensors for Leak Localisation in Water Distribution Networks (FF, BE, SM), pp. 3642–3647.
ICPR-2014-GoldhammerDBGS: Pedestrian’s Trajectory Forecast in Public Traffic with Artificial Neural Networks (MG, KD, UB, AG, BS), pp. 4110–4115.
ICPR-2014-HafemannOC #recognition #using: Forest Species Recognition Using Deep Convolutional Neural Networks (LGH, LSO, PRC), pp. 1103–1107.
ICPR-2014-HuangHWW #clustering: Deep Embedding Network for Clustering (PH, YH, WW, LW), pp. 1532–1537.
ICPR-2014-HuangW0T #framework: A General Nonlinear Embedding Framework Based on Deep Neural Network (YH, WW, LW, TT), pp. 732–737.
ICPR-2014-IosifidisTP #classification: Semi-supervised Classification of Human Actions Based on Neural Networks (AI, AT, IP), pp. 1336–1341.
ICPR-2014-IwahoriFWB #image: Neural Network Based Image Modification for Shape from Observed SEM Images (YI, KF, RJW, MKB), pp. 2131–2136.
ICPR-2014-KangKYLD #classification #documentation #image: Convolutional Neural Networks for Document Image Classification (LK, JK, PY, YL, DSD), pp. 3168–3172.
ICPR-2014-KunwarPB #online #recognition: Semi-supervised Online Bayesian Network Learner for Handwritten Characters Recognition (RK, UP, MB), pp. 3104–3109.
ICPR-2014-OGormanLY #multi #process: Creating a Unified, Wide-Area Activity Map for Multi-camera Networks (LO, DL, GY), pp. 4588–4593.
ICPR-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.
ICPR-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.
ICPR-2014-TsuchiyaMT: Exemplar Network: A Generalized Mixture Model (CT, TM, AT), pp. 598–603.
ICPR-2014-Wilson #graph #modelling: Graph Signatures for Evaluating Network Models (RCW), pp. 100–105.
ICPR-2014-WuHYWT #image #segmentation: Early Hierarchical Contexts Learned by Convolutional Networks for Image Segmentation (ZW, YH, YY, LW, TT), pp. 1538–1543.
ICPR-2014-XuS #learning #using: Bayesian Network Structure Learning Using Causality (ZX, SNS), pp. 3546–3551.
KDD-2014-00020GMB #community #on the: On the permanence of vertices in network communities (TC, SS, NG, AM, SB), pp. 1396–1405.
KDD-2014-AkibaMK #analysis: Network structural analysis via core-tree-decomposition Publication of this article pending inquiry (TA, TM, KiK), pp. 1476–1485.
KDD-2014-BensonRS #learning #multi #scalability: Learning multifractal structure in large networks (ARB, CR, SS), pp. 1326–1335.
KDD-2014-DongYTYC #mobile #social: Inferring user demographics and social strategies in mobile social networks (YD, YY, JT, YY, NVC), pp. 15–24.
KDD-2014-EmbarPB #framework: A bayesian framework for estimating properties of network diffusions (VRE, RKP, IB), pp. 1216–1225.
KDD-2014-GhoshTLY #community #difference: The interplay between dynamics and networks: centrality, communities, and cheeger inequality (RG, SHT, KL, XY), pp. 1406–1415.
KDD-2014-GuSJWC #estimation #topic: Topic-factorized ideal point estimation model for legislative voting network (YG, YS, NJ, BW, TC), pp. 183–192.
KDD-2014-HerodotouDBOF #locality #realtime #scalability: Scalable near real-time failure localization of data center networks (HH, BD, SB, GO, PF), pp. 1689–1698.
KDD-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.
KDD-2014-KhalilDS #optimisation #scalability: Scalable diffusion-aware optimization of network topology (EBK, BND, LS), pp. 1226–1235.
KDD-2014-KurashimaITS #probability #visualisation: Probabilistic latent network visualization: inferring and embedding diffusion networks (TK, TI, NT, HS), pp. 1236–1245.
KDD-2014-NiTFZ #ranking: Inside the atoms: ranking on a network of networks (JN, HT, WF, XZ), pp. 1356–1365.
KDD-2014-PurohitPKZS #performance #scalability: Fast influence-based coarsening for large networks (MP, BAP, CK, YZ, VSS), pp. 1296–1305.
KDD-2014-RozenshteinAGT #detection #process: Event detection in activity networks (PR, AA, AG, NT), pp. 1176–1185.
KDD-2014-ShakarianSPB #social #source code: Reducing gang violence through network influence based targeting of social programs (PS, JS, WP, JB), pp. 1829–1836.
KDD-2014-SintosT #social #using: Using strong triadic closure to characterize ties in social networks (SS, PT), pp. 1466–1475.
KDD-2014-SpasojevicYRB #multi #named #scalability #social #topic: LASTA: large scale topic assignment on multiple social networks (NS, JY, AR, PB), pp. 1809–1818.
KDD-2014-SunSTLKTY #behaviour #collaboration: Analyzing expert behaviors in collaborative networks (HS, MS, ST, YL, LMK, ST, XY), pp. 1486–1495.
KDD-2014-WangHYL #multi #named: MMRate: inferring multi-aspect diffusion networks with multi-pattern cascades (SW, XH, PSY, ZL), pp. 1246–1255.
KDD-2014-XiaoCT: Differentially private network data release via structural inference (QX, RC, KLT), pp. 911–920.
KDD-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.
KDD-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.
KDD-2014-ZhangTMF #learning: Supervised deep learning with auxiliary networks (JZ, GT, YM, WF), pp. 353–361.
KDD-2014-ZhouL #classification #mining #multi: Activity-edge centric multi-label classification for mining heterogeneous information networks (YZ, LL), pp. 1276–1285.
KDD-2014-ZhuSY #analysis #mining #social: Network mining and analysis for social applications (FZ, HS, XY), p. 1974.
KDIR-2014-JohnsonC #clustering #identification: Mathematical Foundations of Networks Supporting Cluster Identification (JEJ, JWC), pp. 277–285.
KMIS-2014-GuerrucciDAB #approach #information management: Phased Approach to a Knowledge Management Network (DG, RMD, RCA, DB), pp. 101–108.
KMIS-2014-LambriniA14a #challenge #information management #ontology: Challenges and Directions for Knowledge Management in Networks of Aligned Ontologies (SL, KA), pp. 146–152.
KMIS-2014-UrwinPCCPPY #ontology: Reference Ontologies for Global Production Networks (ENU, CP, AFCD, FSC, JMPS, SPG, RIMY), pp. 133–139.
KR-2014-BenferhatT #nondeterminism #reasoning: Reasoning with Uncertain Inputs in Possibilistic Networks (SB, KT).
MLDM-2014-FuMD #classification #multi #performance #towards: Towards the Efficient Recovery of General Multi-Dimensional Bayesian Network Classifier (SF, SM, MCD), pp. 16–30.
MLDM-2014-JavedA #classification #dataset #social #using: Creation of Bi-lingual Social Network Dataset Using Classifiers (IJ, HA), pp. 523–533.
MLDM-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.
MLDM-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.
RecSys-2014-BhattacharyaZGGG #social #twitter: Inferring user interests in the Twitter social network (PB, MBZ, NG, SG, KPG), pp. 357–360.
RecSys-2014-GaoTL #personalisation #recommendation #social: Personalized location recommendation on location-based social networks (HG, JT, HL), pp. 399–400.
RecSys-2014-Vahedian #hybrid #recommendation: Weighted hybrid recommendation for heterogeneous networks (FV), pp. 429–432.
RecSys-2014-ZhangOFL #modelling #scalability #social: Scalable audience targeted models for brand advertising on social networks (KZ, AMO, SF, HL), pp. 341–344.
SEKE-2014-CheMLC #online #protocol #runtime #testing: Testing Network Protocols: formally, at runtime and online (XC, SM, JL, ARC), pp. 90–93.
SEKE-2014-JiangWZD #online #social: Forwarding Links without Browsing Links in Online Social Networks (JJ, XW, LZ, YD), pp. 636–641.
SEKE-2014-WangGZ #predict #using: Artificial neural networks for infectious diarrhea prediction using meteorological factors in Shanghai (YW, JG, ZZ), pp. 501–506.
SIGIR-2014-AnilSS #evolution #kernel #modelling #social #using: Modeling evolution of a social network using temporalgraph kernels (AA, NS, SRS), pp. 1051–1054.
SIGIR-2014-BianYC #microblog #predict: Predicting trending messages and diffusion participants in microblogging network (JB, YY, TSC), pp. 537–546.
SIGIR-2014-HuangTK #distance #people: The role of network distance in linkedin people search (SWH, DT, KK), pp. 867–870.
SIGIR-2014-NguyenL #microblog #on the #predict: On predicting religion labels in microblogging networks (MTN, EPL), pp. 1211–1214.
SIGIR-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.
SKY-2014-ExmanN #performance #recommendation #social: Location-based Fast Recommendation Social Network (IE, EN), pp. 55–62.
HILT-2014-RathjeR #framework #java #model checking #source code: A framework for model checking UDP network programs with Java pathfinder (WR, BR), pp. 81–86.
PLDI-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.
POPL-2014-AndersonFGJKSW #named #semantics: NetkAT: semantic foundations for networks (CJA, NF, AG, JBJ, DK, CS, DW), pp. 113–126.
FSE-2014-Lam #named #social: Omlet: a revolution against big-brother social networks (MSL), p. 1.
FSE-2014-Yang #analysis #open source #overview #perspective #social: Social network analysis in open source software peer review (XY), pp. 820–822.
SAC-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.
SAC-2014-BaeLKWP #privacy #social: Analyzing network privacy preserving methods: a perspective of social network characteristics (DHB, JML, SWK, YW, YP), pp. 331–332.
SAC-2014-BarddalGE #classification #concept #named #social: SFNClassifier: a scale-free social network method to handle concept drift (JPB, HMG, FE), pp. 786–791.
SAC-2014-ChoiKKLK: A new device discovery scheme in lighting control networks (SIC, SJK, IK, SKL, TGK), pp. 1743–1744.
SAC-2014-DhanjalC #learning: Learning reputation in an authorship network (CD, SC), pp. 1724–1726.
SAC-2014-FanC #approximate #framework #scalability #social: An approximate framework for scaling social influence computation in large networks (YCF, HC), pp. 610–615.
SAC-2014-GomesBM #similarity: A similarity model for virtual networks negotiation (RLG, LFB, ERMM), pp. 489–494.
SAC-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.
SAC-2014-KimK #community #detection #mobile #social: A detection of overlapping community in mobile social network (PK, SK), pp. 175–179.
SAC-2014-KimKYP #online #social: Sampling in online social networks (SWK, KNK, SHY, SP), pp. 845–849.
SAC-2014-LiuCM #ad hoc #approach #composition #mobile: A low-latency service composition approach in mobile ad hoc networks (CL, JC, FLM), pp. 509–511.
SAC-2014-MazelFF #comparison #detection #diagrams #visual notation: Visual comparison of network anomaly detectors with chord diagrams (JM, RF, KF), pp. 473–480.
SAC-2014-ParkH #analysis #performance: Performance analysis of the golden-SM in the V2V network (MCP, DSH), pp. 1739–1740.
SAC-2014-ParkY #multi #simulation #smarttech: Encountering smartphones in network simulation: a preliminary result on multi-radio multicast (YP, WY), pp. 1727–1728.
SAC-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.
SAC-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.
SAC-2014-RossiLR #algorithm #classification #using: A parameter-free label propagation algorithm using bipartite heterogeneous networks for text classification (RGR, AAL, SOR), pp. 79–84.
SAC-2014-SeffrinRJ #algebra: A dynamic bayesian network for inference of learners’ algebraic knowledge (HMS, GLR, PAJ), pp. 235–240.
SAC-2014-SeoKTC #analysis #framework #social: A structural analysis of literary fictions with social network framework (JS, SHK, HT, HGC), pp. 634–640.
SAC-2014-TesfayHBO #architecture #communication: Cyber-secure communication architecture for active power distribution networks (TTT, JPH, JYLB, PO), pp. 545–552.
SAC-2014-WangW: Wavelength resources based lightpath-level active rerouting in all-optical WDM networks (SWW, CYW), pp. 495–500.
SAC-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.
SAC-2014-WangZC #composition #energy #framework: An energy-aware service composition framework for service-oriented wireless sensor networks (TW, KZ, LC), pp. 408–410.
SAC-2014-YoonY #authentication #using: A biometric-based authenticated key agreement scheme using ECC for wireless sensor networks (EJY, KYY), pp. 699–705.
SAC-2014-ZeilemakerSP #scalability: Large-scale message synchronization in challenged networks (NZ, BS, JAP), pp. 481–488.
CASE-2014-ChenLY #distributed #modelling: Sparse particle filtering for modeling space-time dynamics in distributed sensor networks (YC, GL, HY), pp. 626–631.
CASE-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.
CASE-2014-LinHC #gesture #recognition #using: Human hand gesture recognition using a convolution neural network (HIL, MHH, WKC), pp. 1038–1043.
CASE-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.
DAC-2014-AdirGGS #generative #testing #using: Using a High-Level Test Generation Expert System for Testing In-Car Networks (AA, AG, LG, TS), p. 6.
DAC-2014-AxerTED #bound #performance: Exploiting Shaper Context to Improve Performance Bounds of Ethernet AVB Networks (PA, DT, RE, JD), p. 6.
DAC-2014-HuangYST #assessment #grid #power management: Physics-based Electromigration Assessment for Power Grid Networks (XH, TY, VS, SXDT), p. 6.
DAC-2014-HuWTT #hardware #monitoring #security: System-Level Security for Network Processors with Hardware Monitors (KH, TW, TT, RT), p. 6.
DAC-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.
DAC-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.
DAC-2014-RenMRZ #fault tolerance #using: Fault-tolerant Routing for On-chip Network Without Using Virtual Channels (PR, QM, XR, NZ), p. 6.
DAC-2014-ZhuangWLC #distributed #framework #named #simulation: MATEX: A Distributed Framework for Transient Simulation of Power Distribution Networks (HZ, SHW, JHL, CKC), p. 6.
DATE-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.
DATE-2014-BahrebarS #approach: Improving hamiltonian-based routing methods for on-chip networks: A turn model approach (PB, DS), pp. 1–4.
DATE-2014-BaiS: Isochronous networks by construction (YB, KS), pp. 1–6.
DATE-2014-BanagaayaAST #order #reduction: Implicit index-aware model order reduction for RLC/RC networks (NB, GA, WHAS, CT), pp. 1–6.
DATE-2014-CasamassimaFB #power management: Context aware power management for motion-sensing body area network nodes (FC, EF, LB), pp. 1–6.
DATE-2014-Huang14a #manycore #performance #predict: Leveraging on-chip networks for efficient prediction on multicore coherence (LH), pp. 1–4.
DATE-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.
DATE-2014-MaliukM #framework #prototype: An analog non-volatile neural network platform for prototyping RF BIST solutions (DM, YM), pp. 1–6.
DATE-2014-MottaghiRD #framework #named #performance: RETLab: A fast design-automation framework for arbitrary RET networks (MDM, AR, CD), pp. 1–6.
DATE-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.
DATE-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.
DATE-2014-VillenaS #analysis #performance #variability: Efficient analysis of variability impact on interconnect lines and resistor networks (JFV, LMS), pp. 1–6.
DATE-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.
DATE-2014-ZhangAJC #manycore: Thermal management of manycore systems with silicon-photonic networks (TZ, JLA, AJ, AKC), pp. 1–6.
DATE-2014-ZygmontowiczDCP: Making it harder to unlock an LSIB: Honeytraps and misdirection in a P1687 network (AZ, JD, AC, JCP), pp. 1–6.
HPCA-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.
HPCA-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.
HPDC-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.
HPDC-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.
HPDC-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.
OSDI-2014-KimHZHWWS #abstraction #gpu #named #source code: GPUnet: Networking Abstractions for GPU Programs (SK, SH, XZ, YH, AW, EW, MS), pp. 201–216.
PDP-2014-BenzaidSB #protocol: An Enhanced Secure Pairwise Broadcast Time Synchronization Protocol in Wireless Sensor Networks (CB, AS, NB), pp. 569–573.
PDP-2014-DamP #independence #process: Location Independent Routing in Process Network Overlays (MD, KP), pp. 715–724.
PDP-2014-GhazelS #novel: A Novel QoS-Aware Method Based on Resource Control and Management in NGN Networks (CG, LAS), pp. 288–291.
PDP-2014-GogolevM #classification #random: Density Classification in Asynchronous Random Networks with Faulty Nodes (AG, LM), pp. 256–261.
PDP-2014-Hadim #communication #concurrent #multi #performance: The Multi-level Communication: Minimal Deadlock-Free and Storage Efficient Routing for Torus Networks (MBH), pp. 44–51.
PDP-2014-MinarolliF #distributed #resource management #virtual machine: Distributed Resource Allocation to Virtual Machines via Artificial Neural Networks (DM, BF), pp. 490–499.
PDP-2014-ShalabyRGFK #communication: Hierarchical Network Coding for Collective Communication on HPC Interconnects (AS, MESR, VG, IF, MK), pp. 98–102.
PDP-2014-ZlydarevaMMOO #clustering: Event-Oriented Focal Weight-Based Clustering for Environmental Wireless Sensor Networks (OZ, BFM, WGM, JJO, GMPO), pp. 170–173.
ESOP-2014-Garnock-JonesTF: The Network as a Language Construct (TGJ, STH, MF), pp. 473–492.
FASE-2014-FiadeiroL: Heterogeneous and Asynchronous Networks of Timed Systems (JLF, AL), pp. 79–93.
FoSSaCS-2014-BertrandFS #configuration management #game studies: Playing with Probabilities in Reconfigurable Broadcast Networks (NB, PF, AS), pp. 134–148.
STOC-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.
STOC-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.
TACAS-2014-HerreraWP #query #reduction: Quasi-Equal Clock Reduction: More Networks, More Queries (CH, BW, AP), pp. 295–309.
WRLA-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.
CAV-2014-HuangFMMK #automaton #hybrid #invariant #verification: Invariant Verification of Nonlinear Hybrid Automata Networks of Cardiac Cells (ZH, CF, AM, SM, MZK), pp. 373–390.
ISSTA-2014-GotliebM #named #reduction #testing: FLOWER: optimal test suite reduction as a network maximum flow (AG, DM), pp. 171–180.
VMCAI-2014-AcunaAMS #approach #complexity #heuristic #modelling: Modeling Parsimonious Putative Regulatory Networks: Complexity and Heuristic Approach (VA, AA, AM, AS), pp. 322–336.
HT-2013-HelicSGS #distributed #modelling #navigation: Models of human navigation in information networks based on decentralized search (DH, MS, MG, RS), pp. 89–98.
HT-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.
HT-2013-KangL #information management #social: Structural and cognitive bottlenecks to information access in social networks (JHK, KL), pp. 51–59.
ICDAR-2013-AlvaroSB #classification #hybrid #online: Classification of On-Line Mathematical Symbols with Hybrid Features and Recurrent Neural Networks (FA, JAS, JMB), pp. 1012–1016.
ICDAR-2013-BlucheNK #feature model #recognition #word: Feature Extraction with Convolutional Neural Networks for Handwritten Word Recognition (TB, HN, CK), pp. 285–289.
ICDAR-2013-BreuelUAS #using: High-Performance OCR for Printed English and Fraktur Using LSTM Networks (TMB, AUH, MIAAA, FS), pp. 683–687.
ICDAR-2013-NalisnickB #sentiment: Extracting Sentiment Networks from Shakespeare’s Plays (ETN, HSB), pp. 758–762.
ICDAR-2013-PuriST #learning: Bayesian Network Structure Learning and Inference Methods for Handwriting (MP, SNS, YT), pp. 1320–1324.
ICDAR-2013-SchambachR #learning #sequence: Stabilize Sequence Learning with Recurrent Neural Networks by Forced Alignment (MPS, SFR), pp. 1270–1274.
ICDAR-2013-Ul-HasanARSB #bidirectional #recognition: Offline Printed Urdu Nastaleeq Script Recognition with Bidirectional LSTM Networks (AUH, SBA, SFR, FS, TMB), pp. 1061–1065.
JCDL-2013-SuboticRS #distributed: A distributed archival network for process-oriented autonomic long-term digital preservation (IS, LR, HS), pp. 29–38.
SIGMOD-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.
SIGMOD-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.
SIGMOD-2013-KhuranaD #named #scalability: HiNGE: enabling temporal network analytics at scale (UK, AD), pp. 1089–1092.
SIGMOD-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.
SIGMOD-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.
SIGMOD-2013-VianaM #named #realtime #social: FriendRouter: real-time path finder in social networks (WV, MMM), pp. 1281–1282.
SIGMOD-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.
TPDL-2013-ReinandaUSR: Entity Network Extraction Based on Association Finding and Relation Extraction (RR, MU, FS, MdR), pp. 156–167.
VLDB-2013-GionisJLSW #social: Piggybacking on Social Networks (AG, FJ, VL, MS, IW), pp. 409–420.
VLDB-2013-HendawiBM #framework #named #predict #query #scalability: iRoad: A Framework For Scalable Predictive Query Processing On Road Networks (AMH, JB, MFM), pp. 1262–1265.
VLDB-2013-HuangCLQY #scalability: Top-K Structural Diversity Search in Large Networks (XH, HC, RHL, LQ, JXY), pp. 1618–1629.
VLDB-2013-MokbelS #data transformation #perspective #social: Mobility and Social Networking: A Data Management Perspective (MFM, MS), pp. 1196–1197.
VLDB-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.
ITiCSE-2013-FeasterAZH #algorithm #education #protocol: Serious toys II: teaching networks, protocols, and algorithms (YF, FA, JZ, JOH), pp. 273–278.
ITiCSE-2013-QianYGBT #authentication #learning #mobile #security: Mobile device based authentic learning for computer network and security (KQ, MY, MG, PB, LT), p. 335.
ITiCSE-2013-TrabelsiA #education #generative #using: Using network packet generators and snort rules for teaching denial of service attacks (ZT, LA), pp. 285–290.
CSMR-2013-BorgPR: Analyzing Networks of Issue Reports (MB, DP, PR), pp. 79–88.
CSMR-2013-SurianTLCL #predict: Predicting Project Outcome Leveraging Socio-Technical Network Patterns (DS, YT, DL, HC, EPL), pp. 47–56.
CSMR-2013-ThungBLJ #git #social: Network Structure of Social Coding in GitHub (FT, TFB, DL, LJ), pp. 323–326.
MSR-2013-MacLeanK #commit #dataset #social: Apache commits: social network dataset (ACM, CDK), pp. 135–138.
MSR-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.
ICALP-v2-2013-HenzingerKN #maintenance: Sublinear-Time Maintenance of Breadth-First Spanning Tree in Partially Dynamic Networks (MH, SK, DN), pp. 607–619.
ICALP-v2-2013-JurdzinskiKS #distributed: Distributed Deterministic Broadcasting in Wireless Networks of Weak Devices (TJ, DRK, GS), pp. 632–644.
ICALP-v2-2013-Kleinberg #algorithm #social: Algorithms, Networks, and Social Phenomena (JMK), pp. 1–3.
ICALP-v2-2013-MertziosMCS #constraints #optimisation: Temporal Network Optimization Subject to Connectivity Constraints (GBM, OM, IC, PGS), pp. 657–668.
ICALP-v2-2013-MertziosS #bound #evolution: Strong Bounds for Evolution in Networks (GBM, PGS), pp. 669–680.
LATA-2013-DelzannoT #complexity #decidability #verification: Decidability and Complexity Results for Verification of Asynchronous Broadcast Networks (GD, RT), pp. 238–249.
CIG-2013-Brown #graph #multi #search-based: Examination of graphs in Multiple Agent Genetic Networks for Iterated Prisoner's Dilemma (JAB), pp. 1–8.
CIG-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.
DiGRA-2013-StokesWFW #collaboration #game studies: A Reality Game to Cross Disciplines: Fostering Networks and Collaboration (BS, JW, TF, SW).
FDG-2013-KoutnikCSG #evolution #scalability: Evolving large-scale neural networks for vision-based TORCS (JK, GC, JS, FJG), pp. 206–212.
FDG-2013-RyanSVC #education #game studies #security #using: Network Nightmares: Using games to teach networks and security (WR, JS, DV, JC), pp. 413–416.
CHI-2013-BernsteinBBK #social: Quantifying the invisible audience in social networks (MSB, EB, MB, BK), pp. 21–30.
CHI-2013-BrownMR #interactive #multi #named: MultiNet: reducing interaction overhead in domestic wireless networks (AB, RM, TR), pp. 1569–1578.
CHI-2013-CurmiFSW13a #named #social: HeartLink: open broadcast of live biometric data to social networks (FC, MAF, JS, JW), pp. 1749–1758.
CHI-2013-DunneS #clique #readability #visualisation: Motif simplification: improving network visualization readability with fan, connector, and clique glyphs (CD, BS), pp. 3247–3256.
CHI-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.
CHI-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.
CHI-2013-HowleyN #community: Factors impacting community response in an interest-sharing network (IH, TN), pp. 2283–2286.
CHI-2013-ShiXC #bound #social #using: Using contextual integrity to examine interpersonal information boundary on social network sites (PS, HX, YC), pp. 35–38.
CHI-2013-SpiliotopoulosO #comprehension #facebook #metric #privacy: Understanding motivations for facebook use: usage metrics, network structure, and privacy (TS, IO), pp. 3287–3296.
CSCW-2013-BradyZMB #social: Investigating the appropriateness of social network question asking as a resource for blind users (ELB, YZ, MRM, JPB), pp. 1225–1236.
CSCW-2013-BurtMB: Path dependent network advantage (RSB, JLM, JGB), pp. 1–2.
CSCW-2013-GaoHZ #how #social: Closure vs. structural holes: how social network information and culture affect choice of collaborators (GG, PJH, CZ), pp. 5–18.
CSCW-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.
CSCW-2013-Heck #social: Combining social information for academic networking (TH), pp. 1387–1398.
CSCW-2013-LinF #learning: Opportunities via extended networks for teens’ informal learning (PL, SDF), pp. 1341–1352.
CSCW-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.
DUXU-CXC-2013-Said #social #using: Young Egyptians Use of Social Networks and the January 2011 Revolution (GRES), pp. 38–43.
DUXU-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.
DUXU-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.
DUXU-PMT-2013-KarahasanovicF #2d #approach #behaviour #experience #modelling: Modelling User Behaviour and Experience — The R2D2 Networks Approach (AK, AF), pp. 506–515.
DUXU-PMT-2013-Moallem: Location, Location, Location: About Home Networking Devices Location and Features (AM), pp. 107–114.
DUXU-WM-2013-Gould: Dot, Line, Network: Helping Individuals Make Sense of “New Data” (EWG), pp. 496–505.
DUXU-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.
HCI-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.
HCI-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.
HCI-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.
HCI-IMT-2013-HenschenL #design #interface: A Web-Based Interface for a System That Designs Sensor Networks (LJH, JCL), pp. 688–697.
HCI-UC-2013-BelliniBNP #recommendation: A Static and Dynamic Recommendations System for Best Practice Networks (PB, IB, PN, MP), pp. 259–268.
HCI-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.
HCI-UC-2013-HayashiKO #empirical #social #trust: An Empirical Investigation of Similarity-Driven Trust Dynamics in a Social Network (YH, VVK, HO), pp. 20–28.
HCI-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.
HCI-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.
HCI-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.
HIMI-D-2013-Maeshiro #multi: A Model of Living Organisms to Integrate Multiple Relationship Network Descriptions (TM), pp. 475–483.
HIMI-D-2013-YanagimotoSY #classification #estimation #sentiment #using #word: Word Classification for Sentiment Polarity Estimation Using Neural Network (HY, MS, AY), pp. 669–677.
HIMI-HSM-2013-ByerD #mobile: BARMOTIN- A Voice Controlled Mobile Tourism Information Network for Barbados (DB, CD), pp. 347–354.
HIMI-HSM-2013-ChenHCK #design #social #usability: Usability Study of Icon Designs with Social Network Functions (CHC, WHH, SCC, YYK), pp. 355–362.
HIMI-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.
OCSC-2013-AdlerA #social: The Influence of Social Networking Sites on Participation in the 2012 Presidential Election (RFA, WDA), pp. 233–239.
OCSC-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.
OCSC-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.
OCSC-2013-Eustace #learning: Building and Sustaining a Lifelong Adult Learning Network (KE), pp. 260–268.
OCSC-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.
OCSC-2013-JiangB #approach #case study #multi #social: A Three-Level Approach to the Study of Multi-cultural Social Networking (YJ, OdB), pp. 365–374.
OCSC-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.
OCSC-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.
OCSC-2013-Meiselwitz #assessment #policy #readability #social: Readability Assessment of Policies and Procedures of Social Networking Sites (GM), pp. 67–75.
OCSC-2013-ShiYH #comprehension #matter #motivation #social: Understanding Social Network Sites (SNSs) Preferences: Personality, Motivation, and Happiness Matters (YS, XY, JH), pp. 94–103.
OCSC-2013-WangA #social #using: Adult Learners and Their Use of Social Networking Sites (Y(W, JA), pp. 222–229.
OCSC-2013-YueSC #social: Who Are Seeking Friends? The Portrait of Stranger-Seeker in Social Network Sites (XY, YS, HC), pp. 120–125.
CAiSE-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.
EDOC-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.
ICEIS-v1-2013-BarbosaCRM #estimation: Average Speed Estimation for Road Networks based on GPS Raw Trajectories (IB, MAC, CR, JAFdM), pp. 490–497.
ICEIS-v1-2013-CoelhoAABB #industrial: Deploying Nodes for Industrial Wireless Networks by Artificial Immune Systems Techniques (PHGC, JLMdA, JFMdA, LFdAB, AVdB), pp. 536–540.
ICEIS-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.
ICEIS-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.
ICEIS-v2-2013-NascimentoVCS #ontology #social: Agent-based Electronic Commerce with Ontology Services and Social Network based Support (VN, MJV, AC, NS), pp. 497–504.
ICEIS-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.
ICEIS-v3-2013-MalekanA #collaboration #modelling #process: Business Process Modeling Languages Supporting Collaborative Networks (HSM, HA), pp. 258–266.
ICEIS-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.
CIKM-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.
CIKM-2013-ArifuzzamanKM #algorithm #named #parallel: PATRIC: a parallel algorithm for counting triangles in massive networks (SA, MK, MVM), pp. 529–538.
CIKM-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.
CIKM-2013-BogdanovS #effectiveness #nearest neighbour #scalability: Accurate and scalable nearest neighbors in large networks based on effective importance (PB, AKS), pp. 1009–1018.
CIKM-2013-ChenCC #query: Spatial-temporal query homogeneity for KNN object search on road networks (YJC, KTC, MSC), pp. 1019–1028.
CIKM-2013-FerenceYL #recommendation #social: Location recommendation for out-of-town users in location-based social networks (GF, MY, WCL), pp. 721–726.
CIKM-2013-GuoZZCG #personalisation #social: Personalized influence maximization on social networks (JG, PZ, CZ, YC, LG), pp. 199–208.
CIKM-2013-HashemiNB #approach #learning #retrieval #topic: Expertise retrieval in bibliographic network: a topic dominance learning approach (SHH, MN, HB), pp. 1117–1126.
CIKM-2013-KongZY #multi #social: Inferring anchor links across multiple heterogeneous social networks (XK, JZ, PSY), pp. 179–188.
CIKM-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.
CIKM-2013-McDowellA #classification: Labels or attributes?: rethinking the neighbors for collective classification in sparsely-labeled networks (LM, DWA), pp. 847–852.
CIKM-2013-MengK: Discovering influential authors in heterogeneous academic networks by a co-ranking method (QM, PJK), pp. 1029–1036.
CIKM-2013-MirylenkaP #navigation #topic #wiki: Navigating the topical structure of academic search results via the Wikipedia category network (DM, AP), pp. 891–896.
CIKM-2013-MishraRT #predict #process: Estimating the relative utility of networks for predicting user activities (NM, DMR, PT), pp. 1047–1056.
CIKM-2013-RongM #social: Diffusion of innovations revisited: from social network to innovation network (XR, QM), pp. 499–508.
CIKM-2013-TanGC0Z #detection #named #social: UNIK: unsupervised social network spam detection (ET, LG, SC, XZ, YEZ), pp. 479–488.
CIKM-2013-WangYLZH #summary #topic #word: Content coverage maximization on word networks for hierarchical topic summarization (CW, XY, YL, CZ, JH), pp. 249–258.
CIKM-2013-ZhangDDC #probability #social: Probabilistic solutions of influence propagation on social networks (MZ, CD, CHQD, EC), pp. 429–438.
CIKM-2013-ZhaoLHCH #recommendation #social: Community-based user recommendation in uni-directional social networks (GZ, MLL, WH, WC, HH), pp. 189–198.
CIKM-2013-ZhongLTZ #named