BibSLEIGH — learning tag

learning

learning

Tag #learning

5675 papers:

PADL-2020-NguyenZJXD #artificial reality #named #programming #set: VRASP: A Virtual Reality Environment for Learning Answer Set Programming (VTN, YZ0, KJ, WX, TD), pp. 82–91.
ASPLOS-2020-AngstadtJW #automaton #bound #kernel #legacy #string: Accelerating Legacy String Kernels via Bounded Automata Learning (KA, JBJ, WW), pp. 235–249.
ASPLOS-2020-HuangJ0 #gpu #memory management #named: SwapAdvisor: Pushing Deep Learning Beyond the GPU Memory Limit via Smart Swapping (CCH, GJ, JL0), pp. 1341–1355.
ASPLOS-2020-HuLL0Z0XDLSX #architecture #framework #named: DeepSniffer: A DNN Model Extraction Framework Based on Learning Architectural Hints (XH, LL, SL, LD0, PZ, YJ0, XX, YD, CL, TS, YX), pp. 385–399.
ASPLOS-2020-MireshghallahTR #named #privacy: Shredder: Learning Noise Distributions to Protect Inference Privacy (FM, MT, PR, AJ, DMT, HE), pp. 3–18.
ASPLOS-2020-PengSD0MXYQ #gpu #memory management #named: Capuchin: Tensor-based GPU Memory Management for Deep Learning (XP, XS, HD, HJ0, WM, QX, FY, XQ), pp. 891–905.
CC-2020-BrauckmannGEC #graph #modelling: Compiler-based graph representations for deep learning models of code (AB, AG, SE, JC), pp. 201–211.
CGO-2020-Haj-AliAWSAS #named: NeuroVectorizer: end-to-end vectorization with deep reinforcement learning (AHA, NKA, TLW, YSS, KA, IS), pp. 242–255.
EDM-2019-AiCGZWFW #concept #online #recommendation: Concept-Aware Deep Knowledge Tracing and Exercise Recommendation in an Online Learning System (FA, YC, YG, YZ, ZW, GF, GW).
EDM-2019-AusinABC #induction #policy: Leveraging Deep Reinforcement Learning for Pedagogical Policy Induction in an Intelligent Tutoring System (MSA, HA, TB, MC).
EDM-2019-BroisinH #automation #design #evaluation #programming #semantics: Design and evaluation of a semantic indicator for automatically supporting programming learning (JB, CH).
EDM-2019-CaoPB #analysis #performance: Incorporating Prior Practice Difficulty into Performance Factor Analysis to Model Mandarin Tone Learning (MC, PIPJ, GMB).
EDM-2019-ChoffinPBV #distributed #modelling #named #scheduling #student: DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills (BC, FP, YB, JJV).
EDM-2019-ChopraKMG #difference #gender: Gender Differences in Work-Integrated Learning Assessments (SC, AK, MM, LG).
EDM-2019-DavisRF #difference #student: Individual Differences in Student Learning Aid Usage (AKD, YJR, DF).
EDM-2019-EmondV #3d #performance #predict #visualisation: Visualizing Learning Performance Data and Model Predictions as Objects in a 3D Space (BE, JJV).
EDM-2019-Furr #clustering #interactive #online #visualisation: Visualization and clustering of learner pathways in an interactive online learning environment (DF).
EDM-2019-GagnonLBD: Filtering non-relevant short answers in peer learning applications (VG, AL, SB, MCD).
EDM-2019-GuthrieC #behaviour #online #quality #student: Adding duration-based quality labels to learning events for improved description of students' online learning behavior (MWG, ZC).
EDM-2019-HarmonW #education #online: Measuring Item Teaching Value in an Online Learning Environment (JH, RW).
EDM-2019-HarrakBLB #automation #identification #self: Automatic identification of questions in MOOC forums and association with self-regulated learning (FH, FB, VL, RB).
EDM-2019-Ikeda #analysis #education #quality #using: Learning Feature Analysis for Quality Improvement of Web-Based Teaching Materials Using Mouse Cursor Tracking (MI).
EDM-2019-JiangIDLW #student: Measuring students' thermal comfort and its impact on learning (HJ, MI, SVD, SL, JW).
EDM-2019-JiangP: Binary Q-matrix Learning with dAFM (NJ, ZAP).
EDM-2019-JoYKL #analysis #comparative #education #effectiveness #online #word: A Comparative Analysis of Emotional Words for Learning Effectiveness in Online Education (JJ, YY, GK, HL).
EDM-2019-JuZABC #identification: Identifying Critical Pedagogical Decisions through Adversarial Deep Reinforcement Learning (SJ, GZ, HA, TB, MC).
EDM-2019-KraussMA #modelling #recommendation: Smart Learning Object Recommendations based on Time-Dependent Learning Need Models (CK, AM, SA).
EDM-2019-LiangYZPG #case study #concept #partial order #strict: Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations (CL0, JY, HZ0, BP, CLG).
EDM-2019-MussackFSC #behaviour #problem #similarity #towards: Towards discovering problem similarity through deep learning: combining problem features and user behavior (DM, RF, PS, PCL).
EDM-2019-NazaretskyHA #clustering #education: Kappa Learning: A New Item-Similarity Method for Clustering Educational Items from Response Data (TN, SH, GA).
EDM-2019-NguyenWSM #component #comprehension #game studies #modelling #using: Using Knowledge Component Modeling to Increase Domain Understanding in a Digital Learning Game (HN, YW, JCS, BMM).
EDM-2019-RamirezYCRS #student #towards #using: Toward Instrumenting Makerspaces: Using Motion Sensors to Capture Students' Affective States in Open-Ended Learning Environments (LR, WY, EC, IR, BS).
EDM-2019-Reddick #algorithm #using: Using a Glicko-based Algorithm to Measure In-Course Learning (RR).
EDM-2019-ReillyD #assessment: Exploring Stealth Assessment via Deep Learning in an Open-Ended Virtual Environment (JMR, CD).
EDM-2019-Sher #mobile #using: Anatomy of mobile learners: Using learning analytics to unveil learning in presence of mobile devices (VS).
EDM-2019-SherHG #mobile #power of #predict #student: Investigating effects of considering mobile and desktop learning data on predictive power of learning management system (LMS) features on student success (VS, MH, DG).
EDM-2019-ShimadaMTOTK #optimisation #process #student: Optimizing Assignment of Students to Courses based on Learning Activity Analytics (AS, KM, YT, HO, RiT, SK).
EDM-2019-WeitekampHMRK #predict #student #towards #using: Toward Near Zero-Parameter Prediction Using a Computational Model of Student Learning (DWI, EH, CJM, NR, KRK).
EDM-2019-WhitehillAH #crowdsourcing #predict #what: Do Learners Know What's Good for Them? Crowdsourcing Subjective Ratings of OERs to Predict Learning Gains (JW, CA, BH).
EDM-2019-YangBSHL #detection #student: Active Learning for Student Affect Detection (TYY, RSB, CS, NTH, ASL).
EDM-2019-Yeung #named #using: Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory (CKY).
EDM-2019-ZaidiCDMBR #modelling #student #using: Accurate Modelling of Language Learning Tasks and Students Using Representations of Grammatical Proficiency (AHZ, AC, CD, RM, PB, AR).
EDM-2019-ZhangDYS #student: Student Knowledge Diagnosis on Response Data via the Model of Sparse Factor Learning (YZ, HD, YY, XS).
ICPC-2019-SchnappingerOPF #classification #maintenance #predict #static analysis #tool support: Learning a classifier for prediction of maintainability based on static analysis tools (MS, MHO, AP, AF), pp. 243–248.
ICPC-2019-XieQMZ #named #programming #visual notation: DeepVisual: a visual programming tool for deep learning systems (CX, HQ, LM0, JZ), pp. 130–134.
ICSME-2019-BarbezKG #anti #metric: Deep Learning Anti-Patterns from Code Metrics History (AB, FK, YGG), pp. 114–124.
ICSME-2019-Ha0 #configuration management #fourier: Performance-Influence Model for Highly Configurable Software with Fourier Learning and Lasso Regression (HH, HZ0), pp. 470–480.
ICSME-2019-MillsEBKCH #classification #traceability: Tracing with Less Data: Active Learning for Classification-Based Traceability Link Recovery (CM, JEA, AB, GK, SC, SH), pp. 103–113.
ICSME-2019-OumazizF0BK #product line: Handling Duplicates in Dockerfiles Families: Learning from Experts (MAO, JRF, XB0, TFB, JK), pp. 524–535.
ICSME-2019-PalacioMMBPS #identification #network #using: Learning to Identify Security-Related Issues Using Convolutional Neural Networks (DNP, DM, KM, CBC, DP, CS), pp. 140–144.
ICSME-2019-TufanoWBPWP #how #source code: Learning How to Mutate Source Code from Bug-Fixes (MT, CW, GB, MDP, MW, DP), pp. 301–312.
MSR-2019-HoangDK0U #fault #framework #named #predict: DeepJIT: an end-to-end deep learning framework for just-in-time defect prediction (TH, HKD, YK, DL0, NU), pp. 34–45.
MSR-2019-PerezC #abstract syntax tree #clone detection #detection #syntax: Cross-language clone detection by learning over abstract syntax trees (DP, SC), pp. 518–528.
MSR-2019-TheetenVC #library: Import2vec learning embeddings for software libraries (BT, FV, TVC), pp. 18–28.
SANER-2019-MaJXLLLZ #combinator #named #testing: DeepCT: Tomographic Combinatorial Testing for Deep Learning Systems (LM0, FJX, MX, BL0, LL0, YL0, JZ), pp. 614–618.
SANER-2019-WhiteTMMP #program repair #sorting: Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities (MW, MT, MM, MM, DP), pp. 479–490.
SANER-2019-XieCYLHDZ #approach #graph #named: DeepLink: A Code Knowledge Graph Based Deep Learning Approach for Issue-Commit Link Recovery (RX, LC, WY0, ZL, TH, DD, SZ), pp. 434–444.
SANER-2019-YangASLHCS #industrial #model inference: Improving Model Inference in Industry by Combining Active and Passive Learning (NY, KA, RRHS, LL, DH, LC, AS), pp. 253–263.
SANER-2019-YuBLKYX #empirical #fault #predict #rank: An Empirical Study of Learning to Rank Techniques for Effort-Aware Defect Prediction (XY, KEB, JL0, JWK, XY, ZX), pp. 298–309.
FM-2019-Sheinvald #automaton #infinity: Learning Deterministic Variable Automata over Infinite Alphabets (SS), pp. 633–650.
FM-2019-TapplerA0EL #markov #process: L*-Based Learning of Markov Decision Processes (MT, BKA, GB0, ME, KGL), pp. 651–669.
IFM-2019-DamascenoMS #adaptation #evolution #reuse: Learning to Reuse: Adaptive Model Learning for Evolving Systems (CDND, MRM, AdSS), pp. 138–156.
SEFM-2019-AvellanedaP #approach #automaton #satisfiability: Learning Minimal DFA: Taking Inspiration from RPNI to Improve SAT Approach (FA, AP), pp. 243–256.
AIIDE-2019-BontragerKASST #game studies #network: “Superstition” in the Network: Deep Reinforcement Learning Plays Deceptive Games (PB, AK, DA, MS, CS, JT), pp. 10–16.
AIIDE-2019-FrazierR: Improving Deep Reinforcement Learning in Minecraft with Action Advice (SF, MR), pp. 146–152.
AIIDE-2019-GaoKHT #case study #on the: On Hard Exploration for Reinforcement Learning: A Case Study in Pommerman (CG, BK, PHL, MET), pp. 24–30.
AIIDE-2019-Hernandez-LealK #modelling: Agent Modeling as Auxiliary Task for Deep Reinforcement Learning (PHL, BK, MET), pp. 31–37.
AIIDE-2019-KartalHT #predict: Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning (BK, PHL, MET), pp. 38–44.
AIIDE-2019-KartalHT19a: Action Guidance with MCTS for Deep Reinforcement Learning (BK, PHL, MET), pp. 153–159.
AIIDE-2019-LinXR #named #semantics: GenerationMania: Learning to Semantically Choreograph (ZL, KX, MR), pp. 52–58.
AIIDE-2019-Marino #game studies #programming #realtime #search-based: Learning Strategies for Real-Time Strategy Games with Genetic Programming (JRHM), pp. 219–220.
AIIDE-2019-WangSZ #behaviour #modelling: Beyond Winning and Losing: Modeling Human Motivations and Behaviors with Vector-Valued Inverse Reinforcement Learning (BW, TS, XSZ), pp. 195–201.
AIIDE-2019-XuKZHLS #metaprogramming: Macro Action Selection with Deep Reinforcement Learning in StarCraft (SX, HK, ZZ, RH, YL, HS), pp. 94–99.
CoG-2019-AshleyCKB #evolution: Learning to Select Mates in Evolving Non-playable Characters (DRA, VC, BK, VB), pp. 1–8.
CoG-2019-ChenL #game studies #metaprogramming: Macro and Micro Reinforcement Learning for Playing Nine-ball Pool (YC, YL), pp. 1–4.
CoG-2019-ChenYL #abstraction #game studies #object-oriented #video: Object-Oriented State Abstraction in Reinforcement Learning for Video Games (YC, HY, YL), pp. 1–4.
CoG-2019-DockhornLVBGL #game studies #modelling: Learning Local Forward Models on Unforgiving Games (AD, SML, VV, IB, RDG, DPL), pp. 1–4.
CoG-2019-FendtA #education #game studies #student #using: Using Learning Games to Teach Texas Civil War History to Public Middle School Students (MWF, EA), pp. 1–4.
CoG-2019-GainaS #game studies #video: “Did You Hear That?” Learning to Play Video Games from Audio Cues (RDG, MS), pp. 1–4.
CoG-2019-GeorgiadisLBW #assessment: Learning Analytics Should Analyse the Learning: Proposing a Generic Stealth Assessment Tool (KG, GvL, KB, WW), pp. 1–8.
CoG-2019-HarriesLRHD #3d #benchmark #metric #named: MazeExplorer: A Customisable 3D Benchmark for Assessing Generalisation in Reinforcement Learning (LH, SL, JR, KH, SD), pp. 1–4.
CoG-2019-IlhanGP #education #multi: Teaching on a Budget in Multi-Agent Deep Reinforcement Learning (EI, JG, DPL), pp. 1–8.
CoG-2019-JooK #game studies #how #question #using #visualisation: Visualization of Deep Reinforcement Learning using Grad-CAM: How AI Plays Atari Games? (HTJ, KJK), pp. 1–2.
CoG-2019-KamaldinovM #game studies: Deep Reinforcement Learning in Match-3 Game (IK, IM), pp. 1–4.
CoG-2019-KanagawaK #challenge #named: Rogue-Gym: A New Challenge for Generalization in Reinforcement Learning (YK, TK), pp. 1–8.
CoG-2019-KanervistoH #named: ToriLLE: Learning Environment for Hand-to-Hand Combat (AK, VH), pp. 1–8.
CoG-2019-KatonaSHDBDW #predict #using: Time to Die: Death Prediction in Dota 2 using Deep Learning (AK, RJS, VJH, SD, FB, AD, JAW), pp. 1–8.
CoG-2019-KeehlS: Monster Carlo 2: Integrating Learning and Tree Search for Machine Playtesting (OK, AMS), pp. 1–8.
CoG-2019-KhaustovBM #game studies: Pass in Human Style: Learning Soccer Game Patterns from Spatiotemporal Data (VK, GMB, MM), pp. 1–2.
CoG-2019-Konen #education #game studies #research: General Board Game Playing for Education and Research in Generic AI Game Learning (WK), pp. 1–8.
CoG-2019-LiapisKMSY #multimodal: Fusing Level and Ruleset Features for Multimodal Learning of Gameplay Outcomes (AL, DK, KM, KS, GNY), pp. 1–8.
CoG-2019-LucasDVBGBPMK #approach #game studies: A Local Approach to Forward Model Learning: Results on the Game of Life Game (SML, AD, VV, CB, RDG, IB, DPL, SM, RK), pp. 1–8.
CoG-2019-NaderiBRH #approach: A Reinforcement Learning Approach To Synthesizing Climbing Movements (KN, AB, SR, PH), pp. 1–7.
CoG-2019-NaikJ #agile #development #game studies #gamification: Relax, It's a Game: Utilising Gamification in Learning Agile Scrum Software Development (NN, PJ), pp. 1–4.
CoG-2019-NamI #game studies #generative #using: Generation of Diverse Stages in Turn-Based Role-Playing Game using Reinforcement Learning (SN, KI), pp. 1–8.
CoG-2019-PiergigliRMG #multi: Deep Reinforcement Learning to train agents in a multiplayer First Person Shooter: some preliminary results (DP, LAR, DM, DG), pp. 1–8.
CoG-2019-PleinesZB: Action Spaces in Deep Reinforcement Learning to Mimic Human Input Devices (MP, FZ, VPB), pp. 1–8.
CoG-2019-RebstockSB #policy: Learning Policies from Human Data for Skat (DR, CS, MB), pp. 1–8.
CoG-2019-SoemersPSB #policy #self: Learning Policies from Self-Play with Policy Gradients and MCTS Value Estimates (DJNJS, ÉP, MS, CB), pp. 1–8.
CoG-2019-SpickCW #generative #using: Procedural Generation using Spatial GANs for Region-Specific Learning of Elevation Data (RJS, PC, JAW), pp. 1–8.
CoG-2019-ZhangPFAJ #game studies #lr: 1GBDT, LR & Deep Learning for Turn-based Strategy Game AI (LZ, HP, QF, CA, YJ), pp. 1–8.
CoG-2019-ZuinV #game studies: Learning a Resource Scale for Collectible Card Games (GLZ, AV), pp. 1–8.
DiGRA-2019-KultimaL #game studies: Sami Game Jam - Learning, Exploring, Reflecting and Sharing Indigenous Culture through Game Jamming (AK, OL).
FDG-2019-GuitartTRCP #game studies #predict #video: From non-paying to premium: predicting user conversion in video games with ensemble learning (AG, SHT, AFdR, PPC, ÁP), p. 9.
FDG-2019-JemmaliKBARE #concept #design #game studies #programming #using: Using game design mechanics as metaphors to enhance learning of introductory programming concepts (CJ, EK, SB, MVA, ER, MSEN), p. 5.
FDG-2019-KarthS: Addressing the fundamental tension of PCGML with discriminative learning (IK, AMS), p. 9.
FDG-2019-Ruch #development #education #game studies: Trans-pacific project-based learning: game production curriculum development (AWR), p. 9.
FDG-2019-WangCYPTA #game studies #synthesis: Goal-based progression synthesis in a korean learning game (SW, BC, SY, JYP, NT, EA), p. 9.
VS-Games-2019-Hohl #architecture #game studies #interactive #visualisation: Game-Based Learning - Developing a Business Game for Interactive Architectural Visualization (WH), pp. 1–4.
VS-Games-2019-ZhangBJ #artificial reality #interactive: Exploring Effects of Interactivity on Learning with Interactive Storytelling in Immersive Virtual Reality (LZ, DAB, CNJ), pp. 1–8.
CIKM-2019-00090S #representation: Hyper-Path-Based Representation Learning for Hyper-Networks (JH0, XL0, YS), pp. 449–458.
CIKM-2019-BhutaniZJ #composition #knowledge base #query: Learning to Answer Complex Questions over Knowledge Bases with Query Composition (NB, XZ, HVJ), pp. 739–748.
CIKM-2019-BoiarovT #metric #recognition #scalability: Large Scale Landmark Recognition via Deep Metric Learning (AB, ET), pp. 169–178.
CIKM-2019-BozarthDHJMPPQS #deployment #ubiquitous: Model Asset eXchange: Path to Ubiquitous Deep Learning Deployment (AB, BD, FH, DJ, KM, NP, SP, GdQ, SS, PT, XW, HX0, FRR, VB), pp. 2953–2956.
CIKM-2019-ChengLCHHCMH #named: DIRT: Deep Learning Enhanced Item Response Theory for Cognitive Diagnosis (SC, QL0, EC, ZH, ZH, YC, HM, GH), pp. 2397–2400.
CIKM-2019-ChenJZPNYWLXG #e-commerce: Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds (DC, JJ, WZ0, FP, LN, CY, JW0, HL, JX, KG), pp. 2527–2535.
CIKM-2019-ChenTL #query #social: Query Embedding Learning for Context-based Social Search (YCC, YCT, CTL), pp. 2441–2444.
CIKM-2019-DuanZYZLWWZS0 #mining #multi #summary: Legal Summarization for Multi-role Debate Dialogue via Controversy Focus Mining and Multi-task Learning (XD, YZ, LY, XZ, XL, TW, RW, QZ, CS, FW0), pp. 1361–1370.
CIKM-2019-EladGNKR #personalisation: Learning to Generate Personalized Product Descriptions (GE, IG, SN, BK, KR), pp. 389–398.
CIKM-2019-ElMS #data analysis #named: ATENA: An Autonomous System for Data Exploration Based on Deep Reinforcement Learning (OBE, TM, AS), pp. 2873–2876.
CIKM-2019-FanHZLLW #named #scalability: MONOPOLY: Learning to Price Public Facilities for Revaluing Private Properties with Large-Scale Urban Data (MF, JH, AZ, YL, PL0, HW), pp. 2655–2663.
CIKM-2019-FanZDCSL #approach #graph #identification #network #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-GongZ00XWH #community #detection #developer #online #using: Detecting Malicious Accounts in Online Developer Communities Using Deep Learning (QG, JZ, YC0, QL0, YX, XW, PH), pp. 1251–1260.
CIKM-2019-GuHDM #analysis #named: LinkRadar: Assisting the Analysis of Inter-app Page Links via Transfer Learning (DG, ZH, SD, YM0), pp. 2077–2080.
CIKM-2019-HanMNKURNS #detection #image #using: Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images (CH, KM, TN, YK, FU, LR, HN, SS), pp. 119–127.
CIKM-2019-HosseiniH #feature model #kernel #multi #prototype #representation: Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection (BH, BH), pp. 1863–1872.
CIKM-2019-HuangSZWC #network #self: Similarity-Aware Network Embedding with Self-Paced Learning (CH0, BS, XZ, XW, NVC), pp. 2113–2116.
CIKM-2019-HuangYX #detection #graph: System Deterioration Detection and Root Cause Learning on Time Series Graphs (HH, SY, YX), pp. 2537–2545.
CIKM-2019-JenkinsFWL #multimodal #representation: Unsupervised Representation Learning of Spatial Data via Multimodal Embedding (PJ, AF, SW, ZL), pp. 1993–2002.
CIKM-2019-JiangCBWYN #predict #smarttech: Learning to Predict Human Stress Level with Incomplete Sensor Data from Wearable Devices (JYJ, ZC, ALB, WW0, SDY, DN), pp. 2773–2781.
CIKM-2019-JiangWZSLL #detection #graph #representation: Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation Learning (ZJ, JW, LZ, CS, YL, XL), pp. 289–298.
CIKM-2019-JinOLLLC #graph #semantics #similarity: Learning Region Similarity over Spatial Knowledge Graphs with Hierarchical Types and Semantic Relations (XJ, BO, SL, DL, KHL, LC), pp. 669–678.
CIKM-2019-KangHLY #recommendation: Semi-Supervised Learning for Cross-Domain Recommendation to Cold-Start Users (SK, JH, DL, HY), pp. 1563–1572.
CIKM-2019-KimSRLW #predict: Deep Learning for Blast Furnaces: Skip-Dense Layers Deep Learning Model to Predict the Remaining Time to Close Tap-holes for Blast Furnaces (KK, BS, SHR, SL, SSW), pp. 2733–2741.
CIKM-2019-KuziLSJZ #adaptation #analysis #information retrieval #rank: Analysis of Adaptive Training for Learning to Rank in Information Retrieval (SK, SL, SKKS, PPJ, CZ), pp. 2325–2328.
CIKM-2019-LiuWSL: Loopless Semi-Stochastic Gradient Descent with Less Hard Thresholding for Sparse Learning (XL, BW, FS, HL), pp. 881–890.
CIKM-2019-LiuWYZSMZGZYQ #graph #mobile #optimisation #representation: Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing (ZL, DW, QY, ZZ, YS, JM, WZ, JG, JZ, SY, YQ), pp. 2577–2584.
CIKM-2019-LiuZYCY #generative #refinement: Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System (YL, CZ, XY, YC, PSY), pp. 1643–1652.
CIKM-2019-LiWWLYLW #multi #platform: Partially Shared Adversarial Learning For Semi-supervised Multi-platform User Identity Linkage (CL, SW, HW, YL, PSY, ZL, WW), pp. 249–258.
CIKM-2019-LuoSAZ0 #multi #retrieval: Cross-modal Image-Text Retrieval with Multitask Learning (JL, YS, XA, ZZ, MY0), pp. 2309–2312.
CIKM-2019-LuoZWZ #framework #named #representation: ResumeGAN: An Optimized Deep Representation Learning Framework for Talent-Job Fit via Adversarial Learning (YL, HZ, YW, XZ), pp. 1101–1110.
CIKM-2019-LuYGWLC #clustering #realtime: Reinforcement Learning with Sequential Information Clustering in Real-Time Bidding (JL, CY, XG, LW, CL, GC), pp. 1633–1641.
CIKM-2019-MaAWSCTY #data analysis #graph #similarity: Deep Graph Similarity Learning for Brain Data Analysis (GM, NKA, TLW, DS, MWC, NBTB, PSY), pp. 2743–2751.
CIKM-2019-MaoSSSS #process: Investigating the Learning Process in Job Search: A Longitudinal Study (JM, DS, SS, FS, MS), pp. 2461–2464.
CIKM-2019-NeutatzMA #detection #fault #named: ED2: A Case for Active Learning in Error Detection (FN, MM, ZA), pp. 2249–2252.
CIKM-2019-RaoSPJCTGK #evolution #recommendation: Learning to be Relevant: Evolution of a Course Recommendation System (SR, KS, GP, MJ, SC, VT, JG, DK), pp. 2625–2633.
CIKM-2019-RizosHS #classification: Augment to Prevent: Short-Text Data Augmentation in Deep Learning for Hate-Speech Classification (GR, KH, BWS), pp. 991–1000.
CIKM-2019-ShenTB #graph #representation: GRLA 2019: The first International Workshop on Graph Representation Learning and its Applications (HS, JT, PB), pp. 2997–2998.
CIKM-2019-ShresthaMAV #behaviour #graph #interactive #social: Learning from Dynamic User Interaction Graphs to Forecast Diverse Social Behavior (PS, SM, DA, SV), pp. 2033–2042.
CIKM-2019-SongS0DX0T #automation #feature model #interactive #named #network #self: AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks (WS, CS, ZX0, ZD, YX, MZ0, JT), pp. 1161–1170.
CIKM-2019-TanYHD #multi #segmentation #semantics: Batch Mode Active Learning for Semantic Segmentation Based on Multi-Clue Sample Selection (YT, LY, QH, ZD), pp. 831–840.
CIKM-2019-TaoGFCYZ #game studies #multi #named #online #predict: GMTL: A GART Based Multi-task Learning Model for Multi-Social-Temporal Prediction in Online Games (JT, LG, CF, LC, DY, SZ), pp. 2841–2849.
CIKM-2019-TrittenbachB #detection #multi: One-Class Active Learning for Outlier Detection with Multiple Subspaces (HT, KB), pp. 811–820.
CIKM-2019-Wang0C #graph #reasoning #recommendation: Learning and Reasoning on Graph for Recommendation (XW, XH0, TSC), pp. 2971–2972.
CIKM-2019-WangJH0YZWHWLXG #adaptation #realtime: Learning Adaptive Display Exposure for Real-Time Advertising (WW, JJ, JH, CC0, CY, WZ0, JW0, XH, YW, HL, JX, KG), pp. 2595–2603.
CIKM-2019-WangL #behaviour #network: Spotting Terrorists by Learning Behavior-aware Heterogeneous Network Embedding (PCW, CTL), pp. 2097–2100.
CIKM-2019-WangRCR0R #graph #predict: Order-free Medicine Combination Prediction with Graph Convolutional Reinforcement Learning (SW, PR, ZC, ZR, JM0, MdR), pp. 1623–1632.
CIKM-2019-WeiXZZZC0ZXL #named: CoLight: Learning Network-level Cooperation for Traffic Signal Control (HW, NX, HZ, GZ, XZ, CC, WZ0, YZ, KX, ZL), pp. 1913–1922.
CIKM-2019-WuLZQ #recommendation: Long- and Short-term Preference Learning for Next POI Recommendation (YW, KL, GZ, XQ), pp. 2301–2304.
CIKM-2019-WuPDTZD #distance #graph #network: Long-short Distance Aggregation Networks for Positive Unlabeled Graph Learning (MW, SP, LD, IWT, XZ, BD), pp. 2157–2160.
CIKM-2019-WuWZJ #effectiveness #performance #recommendation: Learning to Effectively Estimate the Travel Time for Fastest Route Recommendation (NW, JW, WXZ, YJ), pp. 1923–1932.
CIKM-2019-XiaoLM #collaboration: Dynamic Collaborative Recurrent Learning (TX, SL, ZM), pp. 1151–1160.
CIKM-2019-XiaoRMSL #metric #personalisation: Dynamic Bayesian Metric Learning for Personalized Product Search (TX, JR, ZM, HS, SL), pp. 1693–1702.
CIKM-2019-XiaWY #comprehension #multi: Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning (JX, CW, MY), pp. 2393–2396.
CIKM-2019-XiongZXL: Learning Traffic Signal Control from Demonstrations (YX, GZ, KX, ZL), pp. 2289–2292.
CIKM-2019-XuHY #graph #network #scalability: Scalable Causal Graph Learning through a Deep Neural Network (CX, HH, SY), pp. 1853–1862.
CIKM-2019-YangDTTZQD #composition #predict #relational #visual notation: Learning Compositional, Visual and Relational Representations for CTR Prediction in Sponsored Search (XY, TD, WT, XT, JZ, SQ, ZD), pp. 2851–2859.
CIKM-2019-ZhangLZLWWX #benchmark #metric #multi #named #representation: Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning (DZ, JL, HZ, YL, LW, PW, HX), pp. 2763–2771.
CIKM-2019-ZhangLZZLWCZ #word: Learning Chinese Word Embeddings from Stroke, Structure and Pinyin of Characters (YZ, YL, JZ, ZZ, XL, WW, ZC, SZ), pp. 1011–1020.
CIKM-2019-ZhangMLZ0MXT #ranking: Context-Aware Ranking by Constructing a Virtual Environment for Reinforcement Learning (JZ, JM, YL, RZ, MZ0, SM, JX0, QT), pp. 1603–1612.
CIKM-2019-ZhangYWH #automation #e-commerce #named #ranking #realtime: Autor3: Automated Real-time Ranking with Reinforcement Learning in E-commerce Sponsored Search Advertising (YZ, ZY, LW, LH), pp. 2499–2507.
CIKM-2019-ZhaoCY #comprehension #e-commerce #query: A Dynamic Product-aware Learning Model for E-commerce Query Intent Understanding (JZ, HC, DY), pp. 1843–1852.
CIKM-2019-ZhaoSSW #graph #named #precise #retrieval: GRAPHENE: A Precise Biomedical Literature Retrieval Engine with Graph Augmented Deep Learning and External Knowledge Empowerment (SZ, CS, AS, FW), pp. 149–158.
CIKM-2019-ZhengXZFWZLXL #contest: Learning Phase Competition for Traffic Signal Control (GZ, YX, XZ, JF, HW, HZ, YL0, KX, ZL), pp. 1963–1972.
CIKM-2019-ZhouJZQJWWYY #multi: Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching (MZ, JJ, WZ0, ZQ, YJ, CW, GW, YY0, JY), pp. 2645–2653.
CIKM-2019-ZouK: Learning to Ask: Question-based Sequential Bayesian Product Search (JZ, EK), pp. 369–378.
CIKM-2019-ZouLAWZ #multi #named #rank: MarlRank: Multi-agent Reinforced Learning to Rank (SZ, ZL, MA, JW0, PZ), pp. 2073–2076.
ECIR-p1-2019-BalikasDMAA #semantics #using: Learning Lexical-Semantic Relations Using Intuitive Cognitive Links (GB, GD, RM, HA, MRA), pp. 3–18.
ECIR-p1-2019-FlorescuJ #graph #representation: A Supervised Keyphrase Extraction System Based on Graph Representation Learning (CF, WJ), pp. 197–212.
ECIR-p2-2019-Landin #recommendation: Learning User and Item Representations for Recommender Systems (AL), pp. 337–342.
ECIR-p2-2019-SyedIGSV #detection #induction #natural language #query: Inductive Transfer Learning for Detection of Well-Formed Natural Language Search Queries (BS, VI, MG0, MS0, VV), pp. 45–52.
ICML-2019-0002CZG #adaptation #invariant #on the: On Learning Invariant Representations for Domain Adaptation (HZ0, RTdC, KZ0, GJG), pp. 7523–7532.
ICML-2019-0002H: Target-Based Temporal-Difference Learning (DL0, NH), pp. 3713–3722.
ICML-2019-0002VBB #performance: Provably Efficient Imitation Learning from Observation Alone (WS0, AV, BB, DB), pp. 6036–6045.
ICML-2019-0002VY #constraints #policy: Batch Policy Learning under Constraints (HML0, CV, YY), pp. 3703–3712.
ICML-2019-AbelsRLNS #multi: Dynamic Weights in Multi-Objective Deep Reinforcement Learning (AA, DMR, TL, AN, DS), pp. 11–20.
ICML-2019-AcharyaSFS #communication #distributed #sublinear: Distributed Learning with Sublinear Communication (JA, CDS, DJF, KS), pp. 40–50.
ICML-2019-AdamsJWS #fault #metric #modelling: Learning Models from Data with Measurement Error: Tackling Underreporting (RA, YJ, XW, SS), pp. 61–70.
ICML-2019-AdelW #approach #named #visual notation: TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning (TA, AW), pp. 71–81.
ICML-2019-AgarwalLS0: Learning to Generalize from Sparse and Underspecified Rewards (RA, CL, DS, MN0), pp. 130–140.
ICML-2019-Allen-ZhuLS #convergence: A Convergence Theory for Deep Learning via Over-Parameterization (ZAZ, YL, ZS), pp. 242–252.
ICML-2019-AllenSST #infinity #prototype: Infinite Mixture Prototypes for Few-shot Learning (KRA, ES, HS, JBT), pp. 232–241.
ICML-2019-AssranLBR #distributed #probability: Stochastic Gradient Push for Distributed Deep Learning (MA, NL, NB, MR), pp. 344–353.
ICML-2019-BalduzziGB0PJG #game studies #symmetry: Open-ended learning in symmetric zero-sum games (DB, MG, YB, WC0, JP, MJ, TG), pp. 434–443.
ICML-2019-BaranchukPSB #graph #similarity: Learning to Route in Similarity Graphs (DB, DP, AS, AB), pp. 475–484.
ICML-2019-BehpourLZ #predict #probability: Active Learning for Probabilistic Structured Prediction of Cuts and Matchings (SB, AL, BDZ), pp. 563–572.
ICML-2019-BelilovskyEO: Greedy Layerwise Learning Can Scale To ImageNet (EB, ME, EO), pp. 583–593.
ICML-2019-BenzingGMMS #approximate #realtime: Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning (FB, MMG, AM, AM, AS), pp. 604–613.
ICML-2019-BhagojiCMC #lens: Analyzing Federated Learning through an Adversarial Lens (ANB, SC, PM, SBC), pp. 634–643.
ICML-2019-BibautMVL #evaluation #performance: More Efficient Off-Policy Evaluation through Regularized Targeted Learning (AB, IM, NV, MJvdL), pp. 654–663.
ICML-2019-BrownGNN: Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations (DSB, WG, PN, SN), pp. 783–792.
ICML-2019-BunneA0J #generative #modelling: Learning Generative Models across Incomparable Spaces (CB, DAM, AK0, SJ), pp. 851–861.
ICML-2019-ByrdL #question #what: What is the Effect of Importance Weighting in Deep Learning? (JB, ZCL), pp. 872–881.
ICML-2019-CaoS #multi #problem: Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem (JC, WS), pp. 912–920.
ICML-2019-ChandakTKJT: Learning Action Representations for Reinforcement Learning (YC, GT, JK, SMJ, PST), pp. 941–950.
ICML-2019-CharoenphakdeeL #on the #symmetry: On Symmetric Losses for Learning from Corrupted Labels (NC, JL, MS), pp. 961–970.
ICML-2019-ChatterjiPB #kernel #online: Online learning with kernel losses (NSC, AP, PLB), pp. 971–980.
ICML-2019-Chen0LJQS #generative #recommendation: Generative Adversarial User Model for Reinforcement Learning Based Recommendation System (XC, SL0, HL, SJ, YQ, LS), pp. 1052–1061.
ICML-2019-ChengVOCYB: Control Regularization for Reduced Variance Reinforcement Learning (RC, AV, GO, SC, YY, JB), pp. 1141–1150.
ICML-2019-ChenJ: Information-Theoretic Considerations in Batch Reinforcement Learning (JC, NJ), pp. 1042–1051.
ICML-2019-ChuBG #functional #probability: Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning (CC, JHB, PWG), pp. 1213–1222.
ICML-2019-CobbeKHKS: Quantifying Generalization in Reinforcement Learning (KC, OK, CH, TK, JS), pp. 1282–1289.
ICML-2019-CohenKM: Learning Linear-Quadratic Regulators Efficiently with only √T Regret (AC, TK, YM), pp. 1300–1309.
ICML-2019-ColasOSFC #composition #motivation #multi #named: CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning (CC, PYO, OS, PF, MC), pp. 1331–1340.
ICML-2019-CortesDGMY #feedback #graph #online: Online Learning with Sleeping Experts and Feedback Graphs (CC, GD, CG, MM, SY), pp. 1370–1378.
ICML-2019-CortesDMZG #graph: Active Learning with Disagreement Graphs (CC, GD, MM, NZ, CG), pp. 1379–1387.
ICML-2019-CreagerMJWSPZ #representation: Flexibly Fair Representation Learning by Disentanglement (EC, DM, JHJ, MAW, KS, TP, RSZ), pp. 1436–1445.
ICML-2019-CutkoskyS #online: Matrix-Free Preconditioning in Online Learning (AC, TS), pp. 1455–1464.
ICML-2019-CvitkovicK #statistics: Minimal Achievable Sufficient Statistic Learning (MC, GK), pp. 1465–1474.
ICML-2019-CvitkovicSA #source code: Open Vocabulary Learning on Source Code with a Graph-Structured Cache (MC, BS, AA), pp. 1475–1485.
ICML-2019-DadashiBTRS: The Value Function Polytope in Reinforcement Learning (RD, MGB, AAT, NLR, DS), pp. 1486–1495.
ICML-2019-Dann0WB #policy #towards: Policy Certificates: Towards Accountable Reinforcement Learning (CD, LL0, WW, EB), pp. 1507–1516.
ICML-2019-DaoGERR #algorithm #linear #performance #using: Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations (TD, AG, ME, AR, CR), pp. 1517–1527.
ICML-2019-DereliOG #algorithm #analysis #biology #kernel #multi: A Multitask Multiple Kernel Learning Algorithm for Survival Analysis with Application to Cancer Biology (OD, CO, MG), pp. 1576–1585.
ICML-2019-DiaconuW #approach: Learning to Convolve: A Generalized Weight-Tying Approach (ND, DEW), pp. 1586–1595.
ICML-2019-DoanMR #analysis #approximate #distributed #finite #linear #multi: Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning (TTD, STM, JR), pp. 1626–1635.
ICML-2019-DoerrVTTD: Trajectory-Based Off-Policy Deep Reinforcement Learning (AD, MV, MT, ST, CD), pp. 1636–1645.
ICML-2019-Duetting0NPR: Optimal Auctions through Deep Learning (PD, ZF0, HN, DCP, SSR), pp. 1706–1715.
ICML-2019-DuklerLLM #generative #modelling: Wasserstein of Wasserstein Loss for Learning Generative Models (YD, WL, ATL, GM), pp. 1716–1725.
ICML-2019-DuN: Task-Agnostic Dynamics Priors for Deep Reinforcement Learning (YD, KN), pp. 1696–1705.
ICML-2019-DunckerBBS #modelling #probability: Learning interpretable continuous-time models of latent stochastic dynamical systems (LD, GB, JB, MS), pp. 1726–1734.
ICML-2019-ElfekiCRE #named #process #using: GDPP: Learning Diverse Generations using Determinantal Point Processes (ME, CC, MR, ME), pp. 1774–1783.
ICML-2019-FatemiSSK: Dead-ends and Secure Exploration in Reinforcement Learning (MF, SS, HvS, SEK), pp. 1873–1881.
ICML-2019-Feige #invariant #multi #representation: Invariant-Equivariant Representation Learning for Multi-Class Data (IF), pp. 1882–1891.
ICML-2019-FoersterSHBDWBB #multi: Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning (JNF, HFS, EH, NB, ID, SW, MB, MB), pp. 1942–1951.
ICML-2019-FranceschiNPH #graph #network: Learning Discrete Structures for Graph Neural Networks (LF, MN, MP, XH), pp. 1972–1982.
ICML-2019-FrancP #nondeterminism #on the #predict: On discriminative learning of prediction uncertainty (VF, DP), pp. 1963–1971.
ICML-2019-FujimotoMP: Off-Policy Deep Reinforcement Learning without Exploration (SF, DM, DP), pp. 2052–2062.
ICML-2019-GamrianG: Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation (SG, YG), pp. 2063–2072.
ICML-2019-GaoJWWYZ #generative: Deep Generative Learning via Variational Gradient Flow (YG, YJ, YW, YW0, CY, SZ), pp. 2093–2101.
ICML-2019-GeladaKBNB #modelling #named #representation: DeepMDP: Learning Continuous Latent Space Models for Representation Learning (CG, SK, JB, ON, MGB), pp. 2170–2179.
ICML-2019-GhadikolaeiGFS #big data #dataset: Learning and Data Selection in Big Datasets (HSG, HGG, CF, MS), pp. 2191–2200.
ICML-2019-GhaziPW #composition #recursion #sketching: Recursive Sketches for Modular Deep Learning (BG, RP, JRW), pp. 2211–2220.
ICML-2019-GilboaB0 #performance #taxonomy: Efficient Dictionary Learning with Gradient Descent (DG, SB, JW0), pp. 2252–2259.
ICML-2019-GillickREEB #sequence: Learning to Groove with Inverse Sequence Transformations (JG, AR, JHE, DE, DB), pp. 2269–2279.
ICML-2019-GolovnevPS: The information-theoretic value of unlabeled data in semi-supervised learning (AG, DP, BS), pp. 2328–2336.
ICML-2019-GreenfeldGBYK #multi: Learning to Optimize Multigrid PDE Solvers (DG, MG, RB, IY, RK), pp. 2415–2423.
ICML-2019-GreffKKWBZMBL #multi #representation: Multi-Object Representation Learning with Iterative Variational Inference (KG, RLK, RK, NW, CB, DZ, LM, MB, AL), pp. 2424–2433.
ICML-2019-GuoSH #dependence #graph #relational: Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs (LG, ZS, WH0), pp. 2505–2514.
ICML-2019-HacohenW #education #network #on the #power of: On The Power of Curriculum Learning in Training Deep Networks (GH, DW), pp. 2535–2544.
ICML-2019-HafnerLFVHLD: Learning Latent Dynamics for Planning from Pixels (DH, TPL, IF, RV, DH, HL, JD), pp. 2555–2565.
ICML-2019-HanS: Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning (SH, YS), pp. 2586–2595.
ICML-2019-HanSDXWSLZ #game studies #multi #video: Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI (LH, PS, YD, JX, QW, XS, HL, TZ), pp. 2576–2585.
ICML-2019-HeidariNG #algorithm #on the #policy #social: On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning (HH, VN, KPG), pp. 2692–2701.
ICML-2019-HendrickxOS #graph: Graph Resistance and Learning from Pairwise Comparisons (JMH, AO, VS), pp. 2702–2711.
ICML-2019-HoferKND #persistent #representation: Connectivity-Optimized Representation Learning via Persistent Homology (CDH, RK, MN, MD), pp. 2751–2760.
ICML-2019-HoLCSA #performance #policy: Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules (DH, EL, XC0, IS, PA), pp. 2731–2741.
ICML-2019-HoulsbyGJMLGAG: Parameter-Efficient Transfer Learning for NLP (NH, AG, SJ, BM, QdL, AG, MA, SG), pp. 2790–2799.
ICML-2019-HuangDGZ: Unsupervised Deep Learning by Neighbourhood Discovery (JH, QD0, SG, XZ), pp. 2849–2858.
ICML-2019-InnesL #problem: Learning Structured Decision Problems with Unawareness (CI, AL), pp. 2941–2950.
ICML-2019-IqbalS #multi: Actor-Attention-Critic for Multi-Agent Reinforcement Learning (SI, FS), pp. 2961–2970.
ICML-2019-IshidaNMS #modelling: Complementary-Label Learning for Arbitrary Losses and Models (TI, GN, AKM, MS), pp. 2971–2980.
ICML-2019-JacqGPP: Learning from a Learner (AJ, MG, AP, OP), pp. 2990–2999.
ICML-2019-JagielskiKMORSU: Differentially Private Fair Learning (MJ, MJK, JM, AO, AR0, SSM, JU), pp. 3000–3008.
ICML-2019-JangLHS #what: Learning What and Where to Transfer (YJ, HL, SJH, JS), pp. 3030–3039.
ICML-2019-JaquesLHGOSLF #motivation #multi #social: Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning (NJ, AL, EH, ÇG, PAO, DS, JZL, NdF), pp. 3040–3049.
ICML-2019-JayRGST #internet: A Deep Reinforcement Learning Perspective on Internet Congestion Control (NJ, NHR, BG, MS, AT), pp. 3050–3059.
ICML-2019-JeongS19a: Learning Discrete and Continuous Factors of Data via Alternating Disentanglement (YJ, HOS), pp. 3091–3099.
ICML-2019-JiangL #logic: Neural Logic Reinforcement Learning (ZJ, SL), pp. 3110–3119.
ICML-2019-KaplanisSC #policy: Policy Consolidation for Continual Reinforcement Learning (CK, MS, CC), pp. 3242–3251.
ICML-2019-KaplanMMS #concept #geometry: Differentially Private Learning of Geometric Concepts (HK, YM, YM, US), pp. 3233–3241.
ICML-2019-KempkaKW #adaptation #algorithm #invariant #linear #modelling #online: Adaptive Scale-Invariant Online Algorithms for Learning Linear Models (MK, WK, MKW), pp. 3321–3330.
ICML-2019-KhadkaMNDTMLT #collaboration: Collaborative Evolutionary Reinforcement Learning (SK, SM, TN, ZD, ET, SM, YL, KT), pp. 3341–3350.
ICML-2019-KipfLDZSGKB #composition #execution #named: CompILE: Compositional Imitation Learning and Execution (TK, YL, HD, VFZ, ASG, EG, PK, PWB), pp. 3418–3428.
ICML-2019-KonstantinovL #robust: Robust Learning from Untrusted Sources (NK, CL), pp. 3488–3498.
ICML-2019-LawLSZ #distance: Lorentzian Distance Learning for Hyperbolic Representations (MTL, RL, JS, RSZ), pp. 3672–3681.
ICML-2019-LawrenceEC #dependence #multi #named #parametricity: DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures (ARL, CHE, NDFC), pp. 3682–3691.
ICML-2019-LiDMMHH #named #network: LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning (HYL, WD, XM, CM, FH, BGH), pp. 3825–3834.
ICML-2019-LiGDVK #graph #network #similarity: Graph Matching Networks for Learning the Similarity of Graph Structured Objects (YL, CG, TD, OV, PK), pp. 3835–3845.
ICML-2019-LiLS #online #rank: Online Learning to Rank with Features (SL, TL, CS), pp. 3856–3865.
ICML-2019-LiLWZG #black box #named #network: 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-LimA #kernel #markov #process #robust: Kernel-Based Reinforcement Learning in Robust Markov Decision Processes (SHL, AA), pp. 3973–3981.
ICML-2019-LiSK #physics: Adversarial camera stickers: A physical camera-based attack on deep learning systems (JL0, FRS, JZK), pp. 3896–3904.
ICML-2019-LiSSG #exponential #kernel #product line: Learning deep kernels for exponential family densities (WL, DJS, HS, AG), pp. 6737–6746.
ICML-2019-LiuS #multi: Sparse Extreme Multi-label Learning with Oracle Property (WL, XS0), pp. 4032–4041.
ICML-2019-LiuSH: The Implicit Fairness Criterion of Unconstrained Learning (LTL, MS, MH), pp. 4051–4060.
ICML-2019-LiuSX #performance: Taming MAML: Efficient unbiased meta-reinforcement learning (HL, RS, CX), pp. 4061–4071.
ICML-2019-LiZWSX #framework: Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting (XL, YZ, TW, RS, CX), pp. 3925–3934.
ICML-2019-LocatelloBLRGSB: Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations (FL, SB, ML, GR, SG, BS, OB), pp. 4114–4124.
ICML-2019-MahloujifarMM #multi: Data Poisoning Attacks in Multi-Party Learning (SM, MM, AM), pp. 4274–4283.
ICML-2019-MalikKSNSE #modelling: Calibrated Model-Based Deep Reinforcement Learning (AM, VK, JS, DN, HS, SE), pp. 4314–4323.
ICML-2019-MannGGHJLS #recommendation: Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems (TAM, SG, AG, HH, RJ, BL, PS), pp. 4324–4332.
ICML-2019-MaryCK: Fairness-Aware Learning for Continuous Attributes and Treatments (JM, CC, NEK), pp. 4382–4391.
ICML-2019-MavrinYKWY #performance: Distributional Reinforcement Learning for Efficient Exploration (BM, HY, LK, KW, YY), pp. 4424–4434.
ICML-2019-MenschBP #geometry: Geometric Losses for Distributional Learning (AM, MB, GP), pp. 4516–4525.
ICML-2019-MetelliGR #configuration management: Reinforcement Learning in Configurable Continuous Environments (AMM, EG, MR), pp. 4546–4555.
ICML-2019-MishneCC: Co-manifold learning with missing data (GM, ECC, RRC), pp. 4605–4614.
ICML-2019-MohriSS: Agnostic Federated Learning (MM, GS, ATS), pp. 4615–4625.
ICML-2019-NabiMS #policy: Learning Optimal Fair Policies (RN, DM, IS), pp. 4674–4682.
ICML-2019-NaganoY0K: A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning (YN, SY, YF0, MK), pp. 4693–4702.
ICML-2019-NamKMPSF #classification #multi #permutation: Learning Context-dependent Label Permutations for Multi-label Classification (JN, YBK, ELM, SP, RS, JF), pp. 4733–4742.
ICML-2019-NedelecKP: Learning to bid in revenue-maximizing auctions (TN, NEK, VP), pp. 4781–4789.
ICML-2019-Nguyen #on the #set: On Connected Sublevel Sets in Deep Learning (QN), pp. 4790–4799.
ICML-2019-NiekerkJER: Composing Value Functions in Reinforcement Learning (BvN, SJ, ACE, BR), pp. 6401–6409.
ICML-2019-NyeHTS #sketching: Learning to Infer Program Sketches (MIN, LBH, JBT, ASL), pp. 4861–4870.
ICML-2019-OglicG #kernel #scalability: Scalable Learning in Reproducing Kernel Krein Spaces (DO, TG0), pp. 4912–4921.
ICML-2019-OymakS #question: Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path? (SO, MS), pp. 4951–4960.
ICML-2019-PaulOW #optimisation #policy #robust: Fingerprint Policy Optimisation for Robust Reinforcement Learning (SP, MAO, SW), pp. 5082–5091.
ICML-2019-PengHSS: Domain Agnostic Learning with Disentangled Representations (XP, ZH, XS, KS), pp. 5102–5112.
ICML-2019-PingPSZRW #normalisation #representation: Differentiable Dynamic Normalization for Learning Deep Representation (LP, ZP, WS, RZ, JR, LW), pp. 4203–4211.
ICML-2019-QuMX: Nonlinear Distributional Gradient Temporal-Difference Learning (CQ, SM, HX), pp. 5251–5260.
ICML-2019-RadanovicDPS #markov #process: Learning to Collaborate in Markov Decision Processes (GR, RD, DCP, AS), pp. 5261–5270.
ICML-2019-RakellyZFLQ #performance #probability: Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables (KR, AZ, CF, SL, DQ), pp. 5331–5340.
ICML-2019-ReslerM #online: Adversarial Online Learning with noise (AR, YM), pp. 5429–5437.
ICML-2019-RollandKISC #performance #probability #testing: Efficient learning of smooth probability functions from Bernoulli tests with guarantees (PR, AK, AI, AS, VC), pp. 5459–5467.
ICML-2019-RowlandDKMBD #statistics: Statistics and Samples in Distributional Reinforcement Learning (MR, RD, SK, RM, MGB, WD), pp. 5528–5536.
ICML-2019-SaunshiPAKK #analysis #representation: A Theoretical Analysis of Contrastive Unsupervised Representation Learning (NS, OP, SA, MK, HK), pp. 5628–5637.
ICML-2019-SchroeterSM #locality: Weakly-Supervised Temporal Localization via Occurrence Count Learning (JS, KAS, ADM), pp. 5649–5659.
ICML-2019-ShahGAD #bias #on the: On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference (RS, NG, PA, ADD), pp. 5670–5679.
ICML-2019-ShaniEM #revisited: Exploration Conscious Reinforcement Learning Revisited (LS, YE, SM), pp. 5680–5689.
ICML-2019-ShenLL: Learning to Clear the Market (WS, SL, RPL), pp. 5710–5718.
ICML-2019-ShenS: Learning with Bad Training Data via Iterative Trimmed Loss Minimization (YS, SS), pp. 5739–5748.
ICML-2019-Shi0 #multi #performance: Fast Direct Search in an Optimally Compressed Continuous Target Space for Efficient Multi-Label Active Learning (WS, QY0), pp. 5769–5778.
ICML-2019-SinglaWFF #approximate #comprehension #higher-order: Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation (SS0, EW, SF, SF), pp. 5848–5856.
ICML-2019-SongK0 #named #robust: SELFIE: Refurbishing Unclean Samples for Robust Deep Learning (HS, MK, JGL0), pp. 5907–5915.
ICML-2019-SonKKHY #multi #named: QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning (KS, DK, WJK, DH, YY), pp. 5887–5896.
ICML-2019-Stickland0 #adaptation #multi #performance: BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning (ACS, IM0), pp. 5986–5995.
ICML-2019-Streeter #linear: Learning Optimal Linear Regularizers (MS), pp. 5996–6004.
ICML-2019-SundinSSVSK: Active Learning for Decision-Making from Imbalanced Observational Data (IS, PS, ES, AV, SS, SK), pp. 6046–6055.
ICML-2019-SuW #distance #metric #sequence: Learning Distance for Sequences by Learning a Ground Metric (BS, YW), pp. 6015–6025.
ICML-2019-SuWSJ #adaptation #evaluation #named #policy: CAB: Continuous Adaptive Blending for Policy Evaluation and Learning (YS, LW, MS, TJ), pp. 6005–6014.
ICML-2019-TesslerEM #robust: Action Robust Reinforcement Learning and Applications in Continuous Control (CT, YE, SM), pp. 6215–6224.
ICML-2019-ThulasidasanBBC #using: Combating Label Noise in Deep Learning using Abstention (ST, TB, JAB, GC, JMY), pp. 6234–6243.
ICML-2019-TranDRC #generative: Bayesian Generative Active Deep Learning (TT, TTD, IDR0, GC), pp. 6295–6304.
ICML-2019-TrouleauEGKT #process: Learning Hawkes Processes Under Synchronization Noise (WT, JE, MG, NK, PT), pp. 6325–6334.
ICML-2019-VarmaSHRR #dependence #modelling: Learning Dependency Structures for Weak Supervision Models (PV, FS, AH, AR, CR), pp. 6418–6427.
ICML-2019-VinayakKVK #estimation #parametricity: Maximum Likelihood Estimation for Learning Populations of Parameters (RKV, WK, GV, SMK), pp. 6448–6457.
ICML-2019-VorobevUGS #ranking: Learning to select for a predefined ranking (AV, AU, GG, PS), pp. 6477–6486.
ICML-2019-Wang0 #modelling: Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models (DW, QL0), pp. 6576–6585.
ICML-2019-WangCAD #estimation #policy #random: Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation (RW, CC, PVA, YD), pp. 6536–6544.
ICML-2019-WangDWK #logic #named #reasoning #satisfiability #using: SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver (PWW, PLD, BW, JZK), pp. 6545–6554.
ICML-2019-WangZ0Q #random #recommendation #robust: Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random (XW, RZ0, YS0, JQ0), pp. 6638–6647.
ICML-2019-WangZXS #on the: On the Generalization Gap in Reparameterizable Reinforcement Learning (HW, SZ, CX, RS), pp. 6648–6658.
ICML-2019-WiqvistMPF #approximate #architecture #network #statistics #summary: Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation (SW, PAM, UP, JF), pp. 6798–6807.
ICML-2019-WonXL: Projection onto Minkowski Sums with Application to Constrained Learning (JHW, JX, KL), pp. 3642–3651.
ICML-2019-WuCBTS: Imitation Learning from Imperfect Demonstration (YHW, NC, HB, VT, MS), pp. 6818–6827.
ICML-2019-WuDSYHSRK #matrix #metric: Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling (SW, AD, SS, FXY, DNHR, DS, AR, SK), pp. 6828–6839.
ICML-2019-XuLZC #graph: Gromov-Wasserstein Learning for Graph Matching and Node Embedding (HX, DL, HZ, LC), pp. 6932–6941.
ICML-2019-XuRDLF: Learning a Prior over Intent via Meta-Inverse Reinforcement Learning (KX, ER, ADD, SL, CF), pp. 6952–6962.
ICML-2019-YangD #proving #theorem: Learning to Prove Theorems via Interacting with Proof Assistants (KY, JD), pp. 6984–6994.
ICML-2019-YinCRB #distributed: Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning (DY, YC0, KR, PLB), pp. 7074–7084.
ICML-2019-YoonSM #adaptation #named #network: TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning (SWY, JS, JM), pp. 7115–7123.
ICML-2019-YoungBN #generative #modelling #synthesis: Learning Neurosymbolic Generative Models via Program Synthesis (HY, OB, MN), pp. 7144–7153.
ICML-2019-YuCGY #graph #named #network: DAG-GNN: DAG Structure Learning with Graph Neural Networks (YY, JC, TG, MY), pp. 7154–7163.
ICML-2019-YunZYLA #analysis #optimisation #statistics: Trimming the l₁ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning (JY, PZ, EY, ACL, AYA), pp. 7242–7251.
ICML-2019-YurochkinAGGHK #network #parametricity: Bayesian Nonparametric Federated Learning of Neural Networks (MY, MA, SG, KHG, TNH, YK), pp. 7252–7261.
ICML-2019-YuSE #multi: Multi-Agent Adversarial Inverse Reinforcement Learning (LY, JS, SE), pp. 7194–7201.
ICML-2019-YuTRKSAZL #distributed #network: Distributed Learning over Unreliable Networks (CY, HT, CR, SK, AS, DA, CZ, JL0), pp. 7202–7212.
ICML-2019-ZablockiBSPG #recognition: Context-Aware Zero-Shot Learning for Object Recognition (EZ, PB, LS, BP, PG), pp. 7292–7303.
ICML-2019-ZanetteB #bound #problem #using: Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds (AZ, EB), pp. 7304–7312.
ICML-2019-ZengLLY #convergence #coordination: Global Convergence of Block Coordinate Descent in Deep Learning (JZ, TTKL, SL, YY0), pp. 7313–7323.
ICML-2019-ZhangHY #named #performance #recognition #visual notation: LatentGNN: Learning Efficient Non-local Relations for Visual Recognition (SZ, XH, SY), pp. 7374–7383.
ICML-2019-ZhangL #incremental #kernel #online #random #sketching: Incremental Randomized Sketching for Online Kernel Learning (XZ, SL), pp. 7394–7403.
ICML-2019-ZhangS #network: Co-Representation Network for Generalized Zero-Shot Learning (FZ, GS), pp. 7434–7443.
ICML-2019-ZhangVSA0L #modelling #named: SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning (MZ, SV, LS, PA, MJJ0, SL), pp. 7444–7453.
ICML-2019-ZhangYT #novel #policy: Learning Novel Policies For Tasks (YZ, WY, GT), pp. 7483–7492.
ICML-2019-ZhaoST #multi: Maximum Entropy-Regularized Multi-Goal Reinforcement Learning (RZ, XS0, VT), pp. 7553–7562.
ICML-2019-ZhuangCO #online #optimisation #probability: Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization (ZZ, AC, FO), pp. 7664–7672.
ICML-2019-ZhuSLHB #fault tolerance #graph: Improved Dynamic Graph Learning through Fault-Tolerant Sparsification (CJZ, SS, KyL, SH, JB), pp. 7624–7633.
ICML-2019-ZhuWS #classification: Learning Classifiers for Target Domain with Limited or No Labels (PZ, HW, VS), pp. 7643–7653.
KDD-2019-BabaevSTU: E.T.-RNN: Applying Deep Learning to Credit Loan Applications (DB, MS, AT, DU), pp. 2183–2190.
KDD-2019-CenZZYZ0 #multi #network #representation: Representation Learning for Attributed Multiplex Heterogeneous Network (YC, XZ, JZ, HY, JZ, JT0), pp. 1358–1368.
KDD-2019-ChenreddyPNCA #named #optimisation: SMOILE: A Shopper Marketing Optimization and Inverse Learning Engine (ARC, PP, SN, RC, RA), pp. 2934–2942.
KDD-2019-Chien #comprehension #mining: Deep Bayesian Mining, Learning and Understanding (JTC), pp. 3197–3198.
KDD-2019-DengRN #graph #predict #social: Learning Dynamic Context Graphs for Predicting Social Events (SD, HR, YN), pp. 1007–1016.
KDD-2019-deWetO: Finding Users Who Act Alike: Transfer Learning for Expanding Advertiser Audiences (Sd, JO), pp. 2251–2259.
KDD-2019-DiSC: Relation Extraction via Domain-aware Transfer Learning (SD, YS, LC), pp. 1348–1357.
KDD-2019-EsfandiariWAR #online #optimisation: Optimizing Peer Learning in Online Groups with Affinities (ME, DW, SAY, SBR), pp. 1216–1226.
KDD-2019-FanZPLZYWWPH #multi: Multi-Horizon Time Series Forecasting with Temporal Attention Learning (CF, YZ, YP, XL, CZ0, RY, DW, WW, JP, HH), pp. 2527–2535.
KDD-2019-FeiTL #multi #predict #word: Hierarchical Multi-Task Word Embedding Learning for Synonym Prediction (HF, ST, PL0), pp. 834–842.
KDD-2019-GaoJ #graph #network #representation: Graph Representation Learning via Hard and Channel-Wise Attention Networks (HG, SJ), pp. 741–749.
KDD-2019-HaldarARXYDZBTC: Applying Deep Learning to Airbnb Search (MH, MA, PR, TX, SY, HD, QZ, NBW, BCT, BMC, TL), pp. 1927–1935.
KDD-2019-HaoCYSW #concept #knowledge base #ontology #representation: Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts (JH, MC, WY, YS, WW), pp. 1709–1719.
KDD-2019-HeLLH #network: Learning Network-to-Network Model for Content-rich Network Embedding (ZH, JL0, NL, YH), pp. 1037–1045.
KDD-2019-HeXZMZY #multi: Off-policy Learning for Multiple Loggers (LH, LX, WZ, ZMM, YZ, DY), pp. 1184–1193.
KDD-2019-HossainR #process #recognition: Active Deep Learning for Activity Recognition with Context Aware Annotator Selection (HMSH, NR), pp. 1862–1870.
KDD-2019-HouCLCY #framework #graph #representation: A Representation Learning Framework for Property Graphs (YH, HC, CL, JC, MCY), pp. 65–73.
KDD-2019-Huang0DLLPST0Y0 #algorithm #network #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-HuFS #network: Adversarial Learning on Heterogeneous Information Networks (BH, YF0, CS), pp. 120–129.
KDD-2019-HughesCZ #generative: Generating Better Search Engine Text Advertisements with Deep Reinforcement Learning (JWH, KhC, RZ), pp. 2269–2277.
KDD-2019-HuH #named #network #set: Sets2Sets: Learning from Sequential Sets with Neural Networks (HH, XH0), pp. 1491–1499.
KDD-2019-HulsebosHBZSKDH #approach #data type #detection #named #semantics: Sherlock: A Deep Learning Approach to Semantic Data Type Detection (MH, KZH, MAB, EZ, AS, TK, ÇD, CAH), pp. 1500–1508.
KDD-2019-InabaFKZ #approach #distance #energy #metric: A Free Energy Based Approach for Distance Metric Learning (SI, CTF, RVK, KZ), pp. 5–13.
KDD-2019-JiaLZKK #how #question #robust #towards: Towards Robust and Discriminative Sequential Data Learning: When and How to Perform Adversarial Training? (XJ, SL0, HZ, SK, VK), pp. 1665–1673.
KDD-2019-JiaSSB #graph: Graph-based Semi-Supervised & Active Learning for Edge Flows (JJ, MTS, SS, ARB), pp. 761–771.
KDD-2019-KeXZBL #framework #named #online #predict: DeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks (GK, ZX, JZ, JB0, TYL), pp. 384–394.
KDD-2019-KillianWSCDT #using: Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data (JAK, BW, AS, VC, BD, MT), pp. 2430–2438.
KDD-2019-Li0WGYK #adaptation #kernel #multi #predict: Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points (ZL, JZ0, QW0, YG, JY, CK), pp. 2848–2856.
KDD-2019-LiuFWWBL #automation #multi: Automating Feature Subspace Exploration via Multi-Agent Reinforcement Learning (KL, YF, PW, LW, RB, XL), pp. 207–215.
KDD-2019-LiuLDCG #named #recommendation: DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation (DL, JL0, BD, JC, RG), pp. 344–352.
KDD-2019-LiuTLZCMW #adaptation: Exploiting Cognitive Structure for Adaptive Learning (QL0, ST, CL, HZ, EC, HM, SW), pp. 627–635.
KDD-2019-LiZY #effectiveness #performance: Efficient and Effective Express via Contextual Cooperative Reinforcement Learning (YL, YZ, QY), pp. 510–519.
KDD-2019-MingXQR #prototype #sequence: Interpretable and Steerable Sequence Learning via Prototypes (YM, PX, HQ, LR), pp. 903–913.
KDD-2019-OhI #detection #using: Sequential Anomaly Detection using Inverse Reinforcement Learning (MhO, GI), pp. 1480–1490.
KDD-2019-PanLW00Z #predict #using: Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning (ZP, YL, WW, YY0, YZ0, JZ), pp. 1720–1730.
KDD-2019-PanMRSF #multi #online #predict: Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising (JP, YM, ALR, YS, AF), pp. 2689–2697.
KDD-2019-ParkLHHLC #quality: Learning Sleep Quality from Daily Logs (SP, CTL, SH, CH, SWL, MC), pp. 2421–2429.
KDD-2019-Qin0Y: Deep Reinforcement Learning with Applications in Transportation (Z(Q, JT0, JY), pp. 3201–3202.
KDD-2019-RawatLY #multi #using: Naranjo Question Answering using End-to-End Multi-task Learning Model (BPSR, FL, HY0), pp. 2547–2555.
KDD-2019-SahooHKWLALH #image #named #recognition: FoodAI: Food Image Recognition via Deep Learning for Smart Food Logging (DS, WH, SK, XW, HL, PA, EPL, SCHH), pp. 2260–2268.
KDD-2019-Salakhutdinov: Integrating Domain-Knowledge into Deep Learning (RS), p. 3176.
KDD-2019-ShangYLQMY #re-engineering #recommendation: Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation (WS, YY, QL, ZQ, YM, JY), pp. 566–576.
KDD-2019-ShenVAAHN #monitoring #smarttech #using: Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning (YS, MV, AA, AA, AYH, AYN), pp. 1909–1916.
KDD-2019-SpathisRFMR #multi #self #sequence: Sequence Multi-task Learning to Forecast Mental Wellbeing from Sparse Self-reported Data (DS, SSR, KF, CM, JR), pp. 2886–2894.
KDD-2019-SuzukiWN #scheduling: TV Advertisement Scheduling by Learning Expert Intentions (YS, WMW, IN), pp. 3071–3081.
KDD-2019-TangXWZL #multi: Retaining Privileged Information for Multi-Task Learning (FT, CX, FW, JZ, LWHL), pp. 1369–1377.
KDD-2019-TokuiOANOSSUVV #framework #named #research: Chainer: A Deep Learning Framework for Accelerating the Research Cycle (ST, RO, TA, YN, TO, SS, SS, KU, BV, HYV), pp. 2002–2011.
KDD-2019-WangFXL #mobile #profiling #representation: Adversarial Substructured Representation Learning for Mobile User Profiling (PW, YF, HX, XL), pp. 130–138.
KDD-2019-WangLCJ #effectiveness #game studies #performance #representation #retrieval: Effective and Efficient Sports Play Retrieval with Deep Representation Learning (ZW, CL, GC, CJ), pp. 499–509.
KDD-2019-WangLZ #adaptation #ambiguity #graph: Adaptive Graph Guided Disambiguation for Partial Label Learning (DBW, LL0, MLZ), pp. 83–91.
KDD-2019-WangQWLGZHZCZ #game studies: A Minimax Game for Instance based Selective Transfer Learning (BW, MQ, XW, YL, YG, XZ, JH0, BZ, DC, JZ), pp. 34–43.
KDD-2019-WangXLLCDWS #framework #multi #named #network #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-WangYCZ #convergence #performance: ADMM for Efficient Deep Learning with Global Convergence (JW, FY, XC0, LZ0), pp. 111–119.
KDD-2019-WeiCZWGXL #coordination #named #network: PressLight: Learning Max Pressure Control to Coordinate Traffic Signals in Arterial Network (HW, CC, GZ, KW, VVG, KX, ZL), pp. 1290–1298.
KDD-2019-XieH: Learning Class-Conditional GANs with Active Sampling (MKX, SJH), pp. 998–1006.
KDD-2019-XuTZ #kernel #multi: Isolation Set-Kernel and Its Application to Multi-Instance Learning (BCX, KMT, ZHZ), pp. 941–949.
KDD-2019-YangZZX0 #adaptation #capacity #incremental #modelling #scalability: Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability (YY, DWZ, DCZ, HX, YJ0), pp. 74–82.
KDD-2019-YaoCC #clustering #multi #robust: Robust Task Grouping with Representative Tasks for Clustered Multi-Task Learning (YY, JC0, HC), pp. 1408–1417.
KDD-2019-YoshidaTK #graph #metric #mining: Learning Interpretable Metric between Graphs: Convex Formulation and Computation with Graph Mining (TY, IT, MK), pp. 1026–1036.
KDD-2019-YuGNCPH #constraints #incremental: Tackle Balancing Constraint for Incremental Semi-Supervised Support Vector Learning (SY, BG, KN, HC, JP, HH), pp. 1587–1595.
KDD-2019-ZhaiWTPR #visual notation: Learning a Unified Embedding for Visual Search at Pinterest (AZ, HYW, ET, DHP, CR), pp. 2412–2420.
KDD-2019-ZhangFWL0: Unifying Inter-region Autocorrelation and Intra-region Structures for Spatial Embedding via Collective Adversarial Learning (YZ, YF, PW, XL, YZ0), pp. 1700–1708.
KDD-2019-ZhangYY #robust: Adversarial Variational Embedding for Robust Semi-supervised Learning (XZ0, LY, FY), pp. 139–147.
KDD-2019-ZhangZJZ: Learning from Incomplete and Inaccurate Supervision (ZyZ, PZ, YJ0, ZHZ), pp. 1017–1025.
KDD-2019-ZhaoDSZLX #multi #network #relational: Multiple Relational Attention Network for Multi-task Learning (JZ, BD, LS, FZ, WL, HX), pp. 1123–1131.
KDD-2019-ZhaoZWGGQNCL #multi #personalisation: Personalized Attraction Enhanced Sponsored Search with Multi-task Learning (WZ0, BZ, BW, ZG, WG, GQ, WN, JC, HL), pp. 2632–2642.
KDD-2019-ZhouGHZXJLX #collaboration #framework #refinement: A Collaborative Learning Framework to Tag Refinement for Points of Interest (JZ, SG, RH, DZ, JX, AJ, YL, HX), pp. 1752–1761.
KDD-2019-ZhouM0H #education #optimisation: Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching (YZ, FM, JG0, JH), pp. 3231–3232.
KDD-2019-ZouXDS0Y #recommendation: Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems (LZ, LX, ZD, JS, WL0, DY), pp. 2810–2818.
MoDELS-2019-BencomoP #modelling #named #ram #runtime #using: RaM: Causally-Connected and Requirements-Aware Runtime Models using Bayesian Learning (NB, LHGP), pp. 216–226.
Onward-2019-BaniassadBHKA #design: Learning to listen for design (ELAB, IB, RH, GK, MA), pp. 179–186.
Onward-2019-CambroneroDV0WR #re-engineering: Active learning for software engineering (JPC, THYD, NV, JS0, JW, MCR), pp. 62–78.
OOPSLA-2019-BaderSP0 #automation #debugging #named: Getafix: learning to fix bugs automatically (JB, AS, MP, SC0), p. 27.
OOPSLA-2019-CambroneroR #named #source code: AL: autogenerating supervised learning programs (JPC, MCR), p. 28.
OOPSLA-2019-ChenWFBD #relational #using #verification: Relational verification using reinforcement learning (JC, JW, YF, OB, ID), p. 30.
OOPSLA-2019-LiWNN #debugging #detection #network #representation: Improving bug detection via context-based code representation learning and attention-based neural networks (YL, SW0, TNN, SVN), p. 30.
OOPSLA-2019-WuCHS0 #approach #fault #generative #precise #specification: Generating precise error specifications for C: a zero shot learning approach (BW, JPCI, YH, AS, SC0), p. 30.
PLATEAU-2019-ZhaoF0I #live programming #network #programming #visualisation: Live Programming Environment for Deep Learning with Instant and Editable Neural Network Visualization (CZ, TF, JK0, TI), p. 5.
PLDI-2019-0001R #database #modelling #using: Using active learning to synthesize models of applications that access databases (JS0, MCR), pp. 269–285.
PLDI-2019-AstorgaMSWX #generative: Learning stateful preconditions modulo a test generator (AA, PM, SS, SW, TX0), pp. 775–787.
PLDI-2019-EberhardtSRV #alias #api #specification: Unsupervised learning of API aliasing specifications (JE, SS, VR, MTV), pp. 745–759.
PLDI-2019-ZhuXMJ #framework #induction #synthesis: An inductive synthesis framework for verifiable reinforcement learning (HZ0, ZX, SM, SJ), pp. 686–701.
POPL-2019-AlonZLY #distributed #named: code2vec: learning distributed representations of code (UA0, MZ, OL, EY), p. 29.
SAS-2019-NeiderS0M #algorithm #invariant #named: Sorcar: Property-Driven Algorithms for Learning Conjunctive Invariants (DN, SS, PG0, PM), pp. 323–346.
ASE-2019-GuoCXMHLLZL #deployment #development #empirical #framework #platform #towards: An Empirical Study Towards Characterizing Deep Learning Development and Deployment Across Different Frameworks and Platforms (QG, SC, XX, LM0, QH, HL, YL0, JZ, XL), pp. 810–822.
ASE-2019-Hu0XY0Z #framework #mutation testing #testing: DeepMutation++: A Mutation Testing Framework for Deep Learning Systems (QH, LM0, XX, BY, YL0, JZ), pp. 1158–1161.
ASE-2019-NejadgholiY #approximate #case study #library #testing: A Study of Oracle Approximations in Testing Deep Learning Libraries (MN, JY0), pp. 785–796.
ASE-2019-SaifullahAR #api: Learning from Examples to Find Fully Qualified Names of API Elements in Code Snippets (CMKS, MA, CKR), pp. 243–254.
ASE-2019-WanSSXZ0Y #multi #network #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-ZhangC #adaptation #approach #modelling #named: Apricot: A Weight-Adaptation Approach to Fixing Deep Learning Models (HZ, WKC), pp. 376–387.
ASE-2019-ZhangYFSL0 #modelling #named #visualisation: NeuralVis: Visualizing and Interpreting Deep Learning Models (XZ, ZY, YF0, QS, JL, ZC0), pp. 1106–1109.
ASE-2019-ZhengFXS0HMLSC #automation #game studies #named #online #testing #using: Wuji: Automatic Online Combat Game Testing Using Evolutionary Deep Reinforcement Learning (YZ, CF, XX, TS, LM0, JH, ZM, YL0, RS, YC), pp. 772–784.
ESEC-FSE-2019-BuiYJ #api #named: SAR: learning cross-language API mappings with little knowledge (NDQB, YY, LJ), pp. 796–806.
ESEC-FSE-2019-CambroneroLKS0 #code search: When deep learning met code search (JC, HL, SK, KS, SC0), pp. 964–974.
ESEC-FSE-2019-ChenCLML #analysis #approach #named #re-engineering #sentiment: SEntiMoji: an emoji-powered learning approach for sentiment analysis in software engineering (ZC, YC, XL, QM, XL), pp. 841–852.
ESEC-FSE-2019-DuXLM0Z #analysis #modelling #named: DeepStellar: model-based quantitative analysis of stateful deep learning systems (XD, XX, YL0, LM0, YL0, JZ), pp. 477–487.
ESEC-FSE-2019-IslamNPR #debugging: A comprehensive study on deep learning bug characteristics (MJI, GN, RP, HR), pp. 510–520.
ESEC-FSE-2019-Kwiatkowska #robust #safety: Safety and robustness for deep learning with provable guarantees (keynote) (MK), p. 2.
ESEC-FSE-2019-MesbahRJGA #compilation #fault #named: DeepDelta: learning to repair compilation errors (AM, AR, EJ, NG, EA), pp. 925–936.
ESEC-FSE-2019-WuJYBSPX #grammar inference #named: REINAM: reinforcement learning for input-grammar inference (ZW, EJ, WY0, OB, DS, JP, TX0), pp. 488–498.
ESEC-FSE-2019-Zhou0X0JLXH #fault #locality #predict: Latent error prediction and fault localization for microservice applications by learning from system trace logs (XZ, XP0, TX, JS0, CJ, DL, QX, CH), pp. 683–694.
ICSE-2019-FanLLWNZL #analysis #android #graph #using: Graph embedding based familial analysis of Android malware using unsupervised learning (MF, XL, JL0, MW, CN, QZ, TL0), pp. 771–782.
ICSE-2019-KimFY #testing #using: Guiding deep learning system testing using surprise adequacy (JK, RF, SY), pp. 1039–1049.
ICSE-2019-Liu0BKKKKT #consistency: Learning to spot and refactor inconsistent method names (KL0, DK0, TFB, TyK, KK, AK, SK, YLT), pp. 1–12.
ICSE-2019-PhamLQT #debugging #detection #library #locality #named #validation: CRADLE: cross-backend validation to detect and localize bugs in deep learning libraries (HVP, TL, WQ, LT0), pp. 1027–1038.
ICSE-2019-TufanoPWBP #on the: On learning meaningful code changes via neural machine translation (MT, JP, CW, GB, DP), pp. 25–36.
ICSE-2019-WeiLC #android #correlation #detection #named: Pivot: learning API-device correlations to facilitate Android compatibility issue detection (LW, YL, SCC), pp. 878–888.
ASPLOS-2019-ChoOPJL #named: FA3C: FPGA-Accelerated Deep Reinforcement Learning (HC, PO, JP, WJ, JL), pp. 499–513.
ASPLOS-2019-SivathanuCSZ #named #predict: Astra: Exploiting Predictability to Optimize Deep Learning (MS, TC, SSS, LZ), pp. 909–923.
CASE-2019-AyoobiCVV #using: Handling Unforeseen Failures Using Argumentation-Based Learning (HA, MC0, RV, BV), pp. 1699–1704.
CASE-2019-CronrathAL: Enhancing Digital Twins through Reinforcement Learning (CC, ARA, BL), pp. 293–298.
CASE-2019-FarooquiF #modelling #synthesis #using: Synthesis of Supervisors for Unknown Plant Models Using Active Learning (AF, MF), pp. 502–508.
CASE-2019-FoxBSG #automation #multi: Multi-Task Hierarchical Imitation Learning for Home Automation (RF, RB, IS, KG), pp. 1–8.
CASE-2019-GamsRNSKSU #quality #visual notation: Robotic Learning for Increased Productivity: Autonomously Improving Speed of Robotic Visual Quality Inspection (AG, SR, BN, JS, RK, JS, AU), pp. 1275–1281.
CASE-2019-GaoZ0 #behaviour #modelling #navigation: Modeling Socially Normative Navigation Behaviors from Demonstrations with Inverse Reinforcement Learning (XG, XZ, MT0), pp. 1333–1340.
CASE-2019-HongCL #locality #mobile #realtime #using: Real-time Visual-Based Localization for Mobile Robot Using Structured-View Deep Learning (YFH, YMC, CHGL), pp. 1353–1358.
CASE-2019-Huang0C #policy: Machine Preventive Replacement Policy for Serial Production Lines Based on Reinforcement Learning (JH0, QC0, NC), pp. 523–528.
CASE-2019-KanekolTPODKM #image #modelling #process: Learning Deep Dynamical Models of a Waste Incineration Plant from In-furnace Images and Process Data (TK, YT, JP, YO, YD, KK, TM), pp. 873–878.
CASE-2019-KazmiNVRC #detection #recognition #using: Vehicle tire (tyre) detection and text recognition using deep learning (WK, IN, GV, PR, AC), pp. 1074–1079.
CASE-2019-LaiL #detection #using: Video-Based Windshield Rain Detection and Wiper Control Using Holistic-View Deep Learning (CCL, CHGL), pp. 1060–1065.
CASE-2019-LiuHS #approach #exponential #scheduling: A new solution approach for flow shop scheduling with an exponential time-dependent learning effect (LL, HH, LS), pp. 468–473.
CASE-2019-LiuZHZWW #estimation #network #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-PotluriD #detection #injection #performance #process: Deep Learning based Efficient Anomaly Detection for Securing Process Control Systems against Injection Attacks (SP, CD), pp. 854–860.
CASE-2019-QianAX0 #performance: Improved Production Performance Through Manufacturing System Learning (YQ, JA, GX, QC0), pp. 517–522.
CASE-2019-RazaL #approach #multi #policy: Constructive Policy: Reinforcement Learning Approach for Connected Multi-Agent Systems (SJAR, ML), pp. 257–262.
CASE-2019-ShkorutaCMR: Iterative learning control for power profile shaping in selective laser melting (AS, WC, SM, SR), pp. 655–660.
CASE-2019-SoniGAS #hybrid #named: HMC: A Hybrid Reinforcement Learning Based Model Compression for Healthcare Applications (RS, JG, GA, VRS), pp. 146–151.
CASE-2019-WangY0 #approach #monitoring: A Deep Learning Approach for Heating and Cooling Equipment Monitoring (YW, CY, WS0), pp. 228–234.
CASE-2019-WuZQX #precise: Deep Reinforcement Learning of Robotic Precision Insertion Skill Accelerated by Demonstrations (XW, DZ, FQ, DX), pp. 1651–1656.
CASE-2019-XuLWZCQ #approach #performance: An Improved GA-KRR Nested Learning Approach for Refrigeration Compressor Performance Forecasting* (CX, XL, JW, JZ, JC, WQ), pp. 622–627.
CASE-2019-XuMZLKZ #adaptation #word: An Adaptive Wordpiece Language Model for Learning Chinese Word Embeddings (BX, LM, LZ, HL, QK, MZ), pp. 812–817.
CASE-2019-YangLYK #classification #realtime: Investigation of Deep Learning for Real-Time Melt Pool Classification in Additive Manufacturing (ZY, YL, HY, SK), pp. 640–647.
CASE-2019-ZhangCZXL #detection #fault: Weld Defect Detection Based on Deep Learning Method (HZ, ZC, CZ, JX, XL), pp. 1574–1579.
CASE-2019-ZhangLGWL #algorithm #classification #taxonomy: A Shapelet Dictionary Learning Algorithm for Time Series Classification (JZ, XL, LG0, LW, GL), pp. 299–304.
CASE-2019-ZhangLWGG #fault #network #using: Fault Diagnosis Using Unsupervised Transfer Learning Based on Adversarial Network (ZZ, XL, LW, LG0, YG), pp. 305–310.
CASE-2019-ZhouWXS00G #approach #modelling #personalisation #predict: A Model-Driven Learning Approach for Predicting the Personalized Dynamic Thermal Comfort in Ordinary Office Environment (YZ, XW, ZX, YS, TL0, CS0, XG), pp. 739–744.
CADE-2019-ChenWAZKZ #named: NIL: Learning Nonlinear Interpolants (MC, JW0, JA, BZ, DK, NZ), pp. 178–196.
CADE-2019-FioriW #modelling: SCL Clause Learning from Simple Models (AF, CW), pp. 233–249.
ICST-2019-KooS0B #automation #generative #named #testing #worst-case: PySE: Automatic Worst-Case Test Generation by Reinforcement Learning (JK, CS, MK0, SB), pp. 136–147.
ICST-2019-WangWZK #alloy: Learning to Optimize the Alloy Analyzer (WW, KW, MZ, SK), pp. 228–239.
ICST-2019-ZhaoLWSH #framework #fuzzing #industrial #named #perspective #protocol: SeqFuzzer: An Industrial Protocol Fuzzing Framework from a Deep Learning Perspective (HZ, ZL, HW, JS, YH), pp. 59–67.
ICTSS-2019-ArcainiGR #regular expression #testing: Regular Expression Learning with Evolutionary Testing and Repair (PA, AG, ER), pp. 22–40.
TAP-2019-AichernigPSW #case study #predict #testing: Predicting and Testing Latencies with Deep Learning: An IoT Case Study (BKA, FP, RS, AW), pp. 93–111.
TAP-2019-PetrenkoA #communication #state machine: Learning Communicating State Machines (AP, FA), pp. 112–128.
JCDL-2018-ColeASSCJ #design #framework #platform #research: Designing a Research Platform for Engaged Learning (NC, AAR, RS, CES, SC, RJ), pp. 315–316.
JCDL-2018-MaiGS #performance #semantics #using: Using Deep Learning for Title-Based Semantic Subject Indexing to Reach Competitive Performance to Full-Text (FM, LG, AS), pp. 169–178.
EDM-2018-AguerrebereCW #deployment #online #process #student: Estimating the Treatment Effect of New Device Deployment on Uruguayan Students' Online Learning Activity (CA, CC, JW).
EDM-2018-AkramMWMBL #assessment #game studies: Improving Stealth Assessment in Game-based Learning with LSTM-based Analytics (BA, WM, ENW, BWM, KB, JCL).
EDM-2018-CarvalhoGMK #online #process: Analyzing the relative learning benefits of completing required activities and optional readings in online courses (PFC, MG, BM, KK).
EDM-2018-ChenLCBC #analysis #behaviour #scalability: Behavioral Analysis at Scale: Learning Course Prerequisite Structures from Learner Clickstreams (WC, ASL, DC, CGB, MC).
EDM-2018-ChopraG #mining: Job Description Mining to Understand Work-Integrated Learning (SC, LG).
EDM-2018-DuDP #analysis #behaviour #named: ELBA: Exceptional Learning Behavior Analysis (XD, WD, MP).
EDM-2018-FangSLCSFGCCPFG #clustering: Clustering the Learning Patterns of Adults with Low Literacy Skills Interacting with an Intelligent Tutoring System (YF, KTS, AL, QC, GS, SF, JG, SC, ZC, PIP, JF, DG, ACG).
EDM-2018-KarumbaiahBS #game studies #predict #student: Predicting Quitting in Students Playing a Learning Game (SK, RSB, VJS).
EDM-2018-KimVG #named #performance #predict #student: GritNet: Student Performance Prediction with Deep Learning (BHK, EV, VG).
EDM-2018-MatayoshiGDUC #adaptation #assessment #testing: Forgetting curves and testing effect in an adaptive learning and assessment system (JM, UG, CD, HU, EC).
EDM-2018-RajendranKCLB #behaviour #predict: Predicting Learning by Analyzing Eye-Gaze Data of Reading Behavior (RR, AK, KEC, DTL, GB).
EDM-2018-ReillyRS #collaboration #multi #using: Exploring Collaboration Using Motion Sensors and Multi-Modal Learning Analytics (JMR, MR, BS).
EDM-2018-SawyerRAL #analysis #behaviour #game studies #problem #student: Filtered Time Series Analyses of Student Problem-Solving Behaviors in Game-based Learning (RS, JPR, RA, JCL).
EDM-2018-SinghSCD #behaviour #modelling #multi #student: Modeling Hint-Taking Behavior and Knowledge State of Students with Multi-Task Learning (HS, SKS, RC, PD).
EDM-2018-TranLCGSBM #design #documentation #generative: Document Chunking and Learning Objective Generation for Instruction Design (KNT, JHL, DC, UG, BS, CJB, MKM).
EDM-2018-WinchellMLGP #predict #student: Textbook annotations as an early predictor of student learning (AW, MM, ASL, PG, HP).
ICPC-2018-LiNJWHW #behaviour #evolution #named: Logtracker: learning log revision behaviors proactively from software evolution history (SL, XN, ZJ, JW, HH, TW0), pp. 178–188.
ICPC-2018-OttAHBAFL #network #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.
MSR-2018-MajumderBBFM08 #case study #mining #performance #stack overflow: 500+ times faster than deep learning: a case study exploring faster methods for text mining stackoverflow (SM, NB, KB, WF0, TM), pp. 554–563.
MSR-2018-OttAHBL08 #approach #identification #image #source code #video: A deep learning approach to identifying source code in images and video (JO, AA, PH, AB, EL), pp. 376–386.
MSR-2018-TufanoWBPWP08 #source code: Deep learning similarities from different representations of source code (MT, CW, GB, MDP, MW, DP), pp. 542–553.
MSR-2018-YinDCVN08 #natural language #stack overflow: Learning to mine aligned code and natural language pairs from stack overflow (PY, BD, EC, BV, GN), pp. 476–486.
SANER-2018-FakhouryANKA #detection #question #smell: Keep it simple: Is deep learning good for linguistic smell detection? (SF, VA, CN, FK, GA), pp. 602–611.
SANER-2018-XuLLZ #analysis #component #fault #hybrid #kernel #predict: Cross-version defect prediction via hybrid active learning with kernel principal component analysis (ZX, JL0, XL, TZ0), pp. 209–220.
FM-2018-AkazakiLYDH #cyber-physical #using: Falsification of Cyber-Physical Systems Using Deep Reinforcement Learning (TA, SL, YY, YD, JH), pp. 456–465.
SEFM-2018-BabaeeGF #framework #predict #runtime #statistics #using #verification: Prevent : A Predictive Run-Time Verification Framework Using Statistical Learning (RB, AG, SF), pp. 205–220.
ICFP-2018-StampoulisC #functional #higher-order #logic programming #prolog #prototype #using: Prototyping a functional language using higher-order logic programming: a functional pearl on learning the ways of λProlog/Makam (AS, AC), p. 30.
AIIDE-2018-LeeTZXDA #architecture #composition: Modular Architecture for StarCraft II with Deep Reinforcement Learning (DL, HT, JOZ, HX, TD, PA), pp. 187–193.
AIIDE-2018-PackardO #case study #user study: A User Study on Learning from Human Demonstration (BP, SO), pp. 208–214.
CIG-2018-AndersenGG #game studies #realtime: Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games (PAA, MG, OCG), pp. 1–8.
CIG-2018-AungBDCKYW #dataset #predict #scalability: Predicting Skill Learning in a Large, Longitudinal MOBA Dataset (MA, VB, AD, PIC, AVK, CY, ARW), pp. 1–7.
CIG-2018-BulitkoD #heuristic #realtime: Anxious Learning in Real-Time Heuristic Search (VB, KD), pp. 1–4.
CIG-2018-DockhornA #approximate #game studies #video: Forward Model Approximation for General Video Game Learning (AD, DA), pp. 1–8.
CIG-2018-GlavinM #experience #using: Skilled Experience Catalogue: A Skill-Balancing Mechanism for Non-Player Characters using Reinforcement Learning (FGG, MGM), pp. 1–8.
CIG-2018-GudmundssonEPNP: Human-Like Playtesting with Deep Learning (SFG, PE, EP, AN, SP, BK, RM, LC), pp. 1–8.
CIG-2018-HarmerGVHBOSN #3d #concurrent #game studies: Imitation Learning with Concurrent Actions in 3D Games (JH, LG, JdV, HH, JB, TO, KS, MN), pp. 1–8.
CIG-2018-JustesenR #automation #education: Automated Curriculum Learning by Rewarding Temporally Rare Events (NJ, SR), pp. 1–8.
CIG-2018-KaczmarekP #interactive #motivation: Promotion of Learning Motivation through Individualization of Learner-Game Interaction (SK, SP), pp. 1–8.
CIG-2018-KowalskiK #regular expression: Regular Language Inference for Learning Rules of Simplified Boardgames (JK, AK), pp. 1–8.
CIG-2018-ShaoZLZ: Learning Battles in ViZDoom via Deep Reinforcement Learning (KS, DZ, NL, YZ), pp. 1–4.
CIG-2018-SpyrouVPAL #personalisation: Exploiting IoT Technologies for Personalized Learning (ES, NV, AP, SA, HCL), pp. 1–8.
CIG-2018-SwiechowskiTJ #algorithm: Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms (MS, TT, AJ), pp. 1–8.
CIG-2018-TavaresC #game studies #realtime: Tabular Reinforcement Learning in Real-Time Strategy Games via Options (ART, LC), pp. 1–8.
CIG-2018-TorradoBT0P #game studies #video: Deep Reinforcement Learning for General Video Game AI (RRT, PB, JT, JL0, DPL), pp. 1–8.
CIG-2018-WoofC #game studies #network: Learning to Play General Video-Games via an Object Embedding Network (WW, KC), pp. 1–8.
CIG-2018-YangO #evaluation #game studies #independence #realtime: Learning Map-Independent Evaluation Functions for Real-Time Strategy Games (ZY, SO), pp. 1–7.
DiGRA-2018-RichardMA #collaboration #contest: Collegiate eSports as Learning Ecologies: Investigating Collaborative Learning and Cognition During Competitions (GTR, ZAM, RWA).
DiGRA-2018-Wu #education #game studies #video: Video Games, Learning, and the Shifting Educational Landscape (HAW).
FDG-2018-Maureira #game studies #named #tool support: CURIO: a game-based learning toolkit for fostering curiosity (MAGM), p. 6.
VS-Games-2018-KutunS #game studies: Rallye Game: Learning by Playing with Racing Cars (BK, WS), pp. 1–2.
VS-Games-2018-Perez-ColadoRFM #game studies #multi: Multi-Level Game Learning Analytics for Serious Games (IJPC, DCR, MFM, IMO, BFM), pp. 1–4.
VS-Games-2018-RallisLGVDD #analysis #artificial reality #game studies #using #visualisation: An Embodied Learning Game Using Kinect and Labanotation for Analysis and Visualization of Dance Kinesiology (IR, AL, IG, AV, ND, AD), pp. 1–8.
CIKM-2018-0013H #consistency #interactive #modelling #multi: Interactions Modeling in Multi-Task Multi-View Learning with Consistent Task Diversity (XL0, JH), pp. 853–861.
CIKM-2018-AiMLC #rank #theory and practice: Unbiased Learning to Rank: Theory and Practice (QA, JM, YL, WBC), pp. 2305–2306.
CIKM-2018-BiessmannSSSL: “Deep” Learning for Missing Value Imputationin Tables with Non-Numerical Data (FB, DS, SS, PS, DL), pp. 2017–2025.
CIKM-2018-DaveZHAK #approach #recommendation #representation: A Combined Representation Learning Approach for Better Job and Skill Recommendation (VSD, BZ, MAH, KA, MK), pp. 1997–2005.
CIKM-2018-DingTZ #generative #graph: Semi-supervised Learning on Graphs with Generative Adversarial Nets (MD, JT, JZ), pp. 913–922.
CIKM-2018-FerroLM0 #continuation #education #rank: Continuation Methods and Curriculum Learning for Learning to Rank (NF0, CL, MM, RP0), pp. 1523–1526.
CIKM-2018-HashemiWKZC18a #identification: Impact of Domain and User's Learning Phase on Task and Session Identification in Smart Speaker Intelligent Assistants (SHH, KW, AEK, IZ, PAC), pp. 1193–1202.
CIKM-2018-JinSLGWZ #multi #realtime: Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising (JJ, CS, HL, KG, JW0, WZ0), pp. 2193–2201.
CIKM-2018-KimLCCK #comprehension #scheduling: Learning User Preferences and Understanding Calendar Contexts for Event Scheduling (DK, JL, DC, JC, JK), pp. 337–346.
CIKM-2018-KrishnanSS #behaviour #online: Insights from the Long-Tail: Learning Latent Representations of Online User Behavior in the Presence of Skew and Sparsity (AK, AS, HS), pp. 297–306.
CIKM-2018-LiuZHL #representation #visual notation: Adversarial Learning of Answer-Related Representation for Visual Question Answering (YL, XZ0, FH, ZL), pp. 1013–1022.
CIKM-2018-LoyolaGS #debugging #locality #rank: Bug Localization by Learning to Rank and Represent Bug Inducing Changes (PL, KG, FS), pp. 657–665.
CIKM-2018-LuoWHYZ #segmentation #semantics: Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning (YL, ZW, ZH, YY0, CZ), pp. 237–246.
CIKM-2018-MedinaVY #online #testing: Online Learning for Non-Stationary A/B Tests (AMM, SV, DY), pp. 317–326.
CIKM-2018-MelidisSN: Learning under Feature Drifts in Textual Streams (DPM, MS, EN), pp. 527–536.
CIKM-2018-MoraesPH #process: Contrasting Search as a Learning Activity with Instructor-designed Learning (FM, SRP, CH), pp. 167–176.
CIKM-2018-NishidaSOAT #comprehension #information retrieval #multi #named: Retrieve-and-Read: Multi-task Learning of Information Retrieval and Reading Comprehension (KN, IS, AO, HA, JT), pp. 647–656.
CIKM-2018-OhSL #graph #multi: Knowledge Graph Completion by Context-Aware Convolutional Learning with Multi-Hop Neighborhoods (BO, SS, KHL), pp. 257–266.
CIKM-2018-OosterhuisR #online #rank: Differentiable Unbiased Online Learning to Rank (HO, MdR), pp. 1293–1302.
CIKM-2018-PandeyKS #recommendation #using: Recommending Serendipitous Items using Transfer Learning (GP0, DK, AS), pp. 1771–1774.
CIKM-2018-PauleMMO #fine-grained #twitter: Learning to Geolocalise Tweets at a Fine-Grained Level (JDGP, YM, CM, IO), pp. 1675–1678.
CIKM-2018-RamanathIPHGOWK #representation #towards: Towards Deep and Representation Learning for Talent Search at LinkedIn (RR, HI, GP, BH, QG, CO, XW, KK, SCG), pp. 2253–2261.
CIKM-2018-RenFZLLZYW #multi #online: Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising (KR, YF, WZ0, SL, JL, YZ, YY0, JW0), pp. 1433–1442.
CIKM-2018-ShenKBQM #clustering #email #multi #query #ranking: Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering (JS, MK, MB, ZQ, DM), pp. 2127–2135.
CIKM-2018-SongZWTZJC #graph #named #rank: TGNet: Learning to Rank Nodes in Temporal Graphs (QS, BZ, YW, LAT, HZ, GJ, HC), pp. 97–106.
CIKM-2018-SuLK #distributed #hybrid #metric: Communication-Efficient Distributed Deep Metric Learning with Hybrid Synchronization (YS, MRL, IK), pp. 1463–1472.
CIKM-2018-WuCYWTZXG: Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising (DW, XC, XY, HW, QT, XZ, JX, KG), pp. 1443–1451.
CIKM-2018-WuLZ #retrieval #semantics #taxonomy: Joint Dictionary Learning and Semantic Constrained Latent Subspace Projection for Cross-Modal Retrieval (JW, ZL, HZ), pp. 1663–1666.
CIKM-2018-WuWL #classification #multi #sentiment: Imbalanced Sentiment Classification with Multi-Task Learning (FW, CW, JL), pp. 1631–1634.
CIKM-2018-WuWL18a #collaboration #detection #microblog #social: Semi-Supervised Collaborative Learning for Social Spammer and Spam Message Detection in Microblogging (FW, CW, JL), pp. 1791–1794.
CIKM-2018-WuZA #classification #graph: A Quest for Structure: Jointly Learning the Graph Structure and Semi-Supervised Classification (XW, LZ, LA), pp. 87–96.
CIKM-2018-XiaJSZWS #modelling #multi #recommendation: Modeling Consumer Buying Decision for Recommendation Based on Multi-Task Deep Learning (QX, PJ, FS, YZ, XW, ZS), pp. 1703–1706.
CIKM-2018-YangS #multi #named #performance: FALCON: A Fast Drop-In Replacement of Citation KNN for Multiple Instance Learning (SY, XS), pp. 67–76.
CIKM-2018-ZamaniDCLK #ranking #representation: From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing (HZ, MD0, WBC, EGLM, JK), pp. 497–506.
CIKM-2018-ZhaoX0ZLZ #approach #comprehension #on the #predict: On Prediction of User Destination by Sub-Trajectory Understanding: A Deep Learning based Approach (JZ, JX, RZ0, PZ, CL, FZ), pp. 1413–1422.
CIKM-2018-ZhuLYZ0W #framework #predict: A Supervised Learning Framework for Prediction of Incompatible Herb Pair in Traditional Chinese Medicine (JZ, YL, SY, SZ, ZY0, CW), pp. 1799–1802.
ECIR-2018-00010VPRP #multi #twitter: Multi-task Learning for Extraction of Adverse Drug Reaction Mentions from Tweets (SG0, MG0, VV, SP, NR, GKP), pp. 59–71.
ECIR-2018-AgrawalA #detection #multi #platform #social #social media: Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms (SA, AA), pp. 141–153.
ECIR-2018-HerreraPP #microblog #retrieval: Learning to Leverage Microblog Information for QA Retrieval (JMH, BP, DP), pp. 507–520.
ECIR-2018-Jalan0V #classification #using: Medical Forum Question Classification Using Deep Learning (RSJ, MG0, VV), pp. 45–58.
ECIR-2018-McDonaldMO #overview #perspective: Active Learning Strategies for Technology Assisted Sensitivity Review (GM, CM, IO), pp. 439–453.
ECIR-2018-NiculaRR #multi: Improving Deep Learning for Multiple Choice Question Answering with Candidate Contexts (BN, SR, TR), pp. 678–683.
ECIR-2018-TianLWWQLLS #semantics #similarity: An Adversarial Joint Learning Model for Low-Resource Language Semantic Textual Similarity (JT, ML, YW, JW, LQ, SL0, JL, LS), pp. 89–101.
ECIR-2018-WilkensZF #documentation #ranking: Document Ranking Applied to Second Language Learning (RW, LZ, CF), pp. 618–624.
ICML-2018-0001JADYD: Hierarchical Imitation and Reinforcement Learning (HML0, NJ, AA, MD, YY, HDI), pp. 2923–2932.
ICML-2018-AbelALL #abstraction: State Abstractions for Lifelong Reinforcement Learning (DA, DA, LL, MLL), pp. 10–19.
ICML-2018-AbelJGKL #policy: Policy and Value Transfer in Lifelong Reinforcement Learning (DA, YJ, SYG, GDK, MLL), pp. 20–29.
ICML-2018-AchlioptasDMG #3d #generative #modelling: Learning Representations and Generative Models for 3D Point Clouds (PA, OD, IM, LJG), pp. 40–49.
ICML-2018-AlaaS18a #automation #kernel #modelling #named #optimisation: AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning (AMA, MvdS), pp. 139–148.
ICML-2018-AlmahairiRSBC: Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data (AA, SR, AS, PB, ACC), pp. 195–204.
ICML-2018-AsadiML #modelling: Lipschitz Continuity in Model-based Reinforcement Learning (KA, DM, MLL), pp. 264–273.
ICML-2018-BalcanDSV #branch: Learning to Branch (MFB, TD, TS, EV), pp. 353–362.
ICML-2018-BalestrieroCGB: Spline Filters For End-to-End Deep Learning (RB, RC, HG, RGB), pp. 373–382.
ICML-2018-BargiacchiVRNH #coordination #graph #multi #problem: Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems (EB, TV, DMR, AN, HvH), pp. 491–499.
ICML-2018-BarretoBQSSHMZM #policy #using: Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement (AB, DB, JQ, TS, DS, MH, DJM, AZ, RM), pp. 510–519.
ICML-2018-BelkinMM #kernel: To Understand Deep Learning We Need to Understand Kernel Learning (MB, SM, SM), pp. 540–548.
ICML-2018-CalandrielloKLV #graph #scalability: Improved Large-Scale Graph Learning through Ridge Spectral Sparsification (DC, IK, AL, MV), pp. 687–696.
ICML-2018-CaoGWSHT #coordination: Adversarial Learning with Local Coordinate Coding (JC, YG, QW, CS, JH, MT), pp. 706–714.
ICML-2018-CharlesP #algorithm: Stability and Generalization of Learning Algorithms that Converge to Global Optima (ZBC, DSP), pp. 744–753.
ICML-2018-Chatterjee: Learning and Memorization (SC), pp. 754–762.
ICML-2018-ChengDH #rank: Extreme Learning to Rank via Low Rank Assumption (MC, ID, CJH), pp. 950–959.
ICML-2018-ChenLW #scalability #using: Scalable Bilinear Learning Using State and Action Features (YC, LL0, MW), pp. 833–842.
ICML-2018-ChenMS: Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations (TC0, MRM, YS), pp. 853–862.
ICML-2018-ChenSWJ: Learning to Explain: An Information-Theoretic Perspective on Model Interpretation (JC, LS, MJW, MIJ), pp. 882–891.
ICML-2018-ChenXG #multi: End-to-End Learning for the Deep Multivariate Probit Model (DC, YX, CPG), pp. 931–940.
ICML-2018-Chierichetti0T #multi: Learning a Mixture of Two Multinomial Logits (FC, RK0, AT), pp. 960–968.
ICML-2018-ChowNG #consistency: Path Consistency Learning in Tsallis Entropy Regularized MDPs (YC, ON, MG), pp. 978–987.
ICML-2018-Co-ReyesLGEAL #self: Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings (JDCR, YL, AG0, BE, PA, SL), pp. 1008–1017.
ICML-2018-ColasSO #algorithm #named: GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms (CC, OS, PYO), pp. 1038–1047.
ICML-2018-CorneilGB #performance: Efficient ModelBased Deep Reinforcement Learning with Variational State Tabulation (DSC, WG, JB), pp. 1057–1066.
ICML-2018-CortesDGMY #online: Online Learning with Abstention (CC, GD, CG, MM, SY), pp. 1067–1075.
ICML-2018-CzarneckiJJHTHO #education: Mix & Match Agent Curricula for Reinforcement Learning (WMC, SMJ, MJ, LH, YWT, NH, SO, RP), pp. 1095–1103.
ICML-2018-DabneyOSM #network: Implicit Quantile Networks for Distributional Reinforcement Learning (WD, GO, DS, RM), pp. 1104–1113.
ICML-2018-DaiKDSS #algorithm #graph: Learning Steady-States of Iterative Algorithms over Graphs (HD, ZK, BD, AJS, LS), pp. 1114–1122.
ICML-2018-DaiS0XHLCS #approximate #convergence #named: SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation (BD, AS, LL0, LX, NH, ZL0, JC, LS), pp. 1133–1142.
ICML-2018-DepewegHDU #composition #nondeterminism #performance: Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning (SD, JMHL, FDV, SU), pp. 1192–1201.
ICML-2018-DibangoyeB #distributed: Learning to Act in Decentralized Partially Observable MDPs (JSD, OB), pp. 1241–1250.
ICML-2018-DietterichTC: Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning (TGD, GT, ZC), pp. 1261–1269.
ICML-2018-DimakopoulouR #concurrent #coordination: Coordinated Exploration in Concurrent Reinforcement Learning (MD, BVR), pp. 1270–1278.
ICML-2018-EfroniDSM #approach: Beyond the One-Step Greedy Approach in Reinforcement Learning (YE, GD, BS, SM), pp. 1386–1395.
ICML-2018-FalahatgarJOPR #ranking: The Limits of Maxing, Ranking, and Preference Learning (MF, AJ, AO, VP, VR), pp. 1426–1435.
ICML-2018-FengWCS #multi #network #parametricity #using: Nonparametric variable importance using an augmented neural network with multi-task learning (JF, BDW, MC, NS), pp. 1495–1504.
ICML-2018-FlorensaHGA #automation #generative: Automatic Goal Generation for Reinforcement Learning Agents (CF, DH, XG, PA), pp. 1514–1523.
ICML-2018-FruitPLO #performance: Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning (RF, MP, AL, RO), pp. 1573–1581.
ICML-2018-GaneaBH: Hyperbolic Entailment Cones for Learning Hierarchical Embeddings (OEG, GB, TH), pp. 1632–1641.
ICML-2018-GaninKBEV #image #source code #using: Synthesizing Programs for Images using Reinforced Adversarial Learning (YG, TK, IB, SMAE, OV), pp. 1652–1661.
ICML-2018-GaoW #network #parallel: Parallel Bayesian Network Structure Learning (TG, DW), pp. 1671–1680.
ICML-2018-GarciaCEd #predict: Structured Output Learning with Abstention: Application to Accurate Opinion Prediction (AG0, CC, SE, FdB), pp. 1681–1689.
ICML-2018-Georgogiannis #fault #taxonomy: The Generalization Error of Dictionary Learning with Moreau Envelopes (AG), pp. 1710–1718.
ICML-2018-GhassamiSKB #design #empirical: Budgeted Experiment Design for Causal Structure Learning (AG, SS, NK, EB), pp. 1719–1728.
ICML-2018-GhoshalH #modelling #polynomial #predict: Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time (AG, JH), pp. 1749–1757.
ICML-2018-GhoshYD #network: Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors (SG, JY, FDV), pp. 1739–1748.
ICML-2018-GilraG #network: Non-Linear Motor Control by Local Learning in Spiking Neural Networks (AG, WG), pp. 1768–1777.
ICML-2018-GoelKM: Learning One Convolutional Layer with Overlapping Patches (SG, ARK, RM), pp. 1778–1786.
ICML-2018-GroverAGBE #multi #policy: Learning Policy Representations in Multiagent Systems (AG, MAS, JKG, YB, HE), pp. 1797–1806.
ICML-2018-GuezWASVWMS: Learning to Search with MCTSnets (AG, TW, IA, KS, OV, DW, RM, DS), pp. 1817–1826.
ICML-2018-HaarnojaHAL #policy: Latent Space Policies for Hierarchical Reinforcement Learning (TH, KH, PA, SL), pp. 1846–1855.
ICML-2018-HaarnojaZAL #probability: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (TH, AZ, PA, SL), pp. 1856–1865.
ICML-2018-HammN #optimisation #performance: K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning (JH, YKN), pp. 1876–1884.
ICML-2018-HashemiSSALCKR #data access #memory management: Learning Memory Access Patterns (MH, KS, JAS, GA, HL, JC, CK, PR), pp. 1924–1933.
ICML-2018-HeinonenYMIL #modelling #process: Learning unknown ODE models with Gaussian processes (MH, CY, HM, JI, HL), pp. 1964–1973.
ICML-2018-Huang0S #markov #modelling #topic: Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling (KH, XF0, NDS), pp. 2073–2082.
ICML-2018-HuangA0S #using: Learning Deep ResNet Blocks Sequentially using Boosting Theory (FH, JTA, JL0, RES), pp. 2063–2072.
ICML-2018-HuNSS #classification #question #robust: Does Distributionally Robust Supervised Learning Give Robust Classifiers? (WH, GN, IS, MS), pp. 2034–2042.
ICML-2018-IcarteKVM #composition #specification #using: Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning (RTI, TQK, RAV, SAM), pp. 2112–2121.
ICML-2018-IglZLWW: Deep Variational Reinforcement Learning for POMDPs (MI, LMZ, TAL, FW, SW), pp. 2122–2131.
ICML-2018-IlseTW #multi: Attention-based Deep Multiple Instance Learning (MI, JMT, MW), pp. 2132–2141.
ICML-2018-JaffeWCKN #approach #modelling: Learning Binary Latent Variable Models: A Tensor Eigenpair Approach (AJ, RW, SC, YK, BN), pp. 2201–2210.
ICML-2018-JawanpuriaM #framework #matrix #rank: A Unified Framework for Structured Low-rank Matrix Learning (PJ, BM), pp. 2259–2268.
ICML-2018-JeongS #performance: Efficient end-to-end learning for quantizable representations (YJ, HOS), pp. 2269–2278.
ICML-2018-JiangEL: Feedback-Based Tree Search for Reinforcement Learning (DRJ, EE, HL), pp. 2289–2298.
ICML-2018-JiangZLLF #data-driven #education #named #network: MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels (LJ0, ZZ, TL, LJL, LFF0), pp. 2309–2318.
ICML-2018-JinKL18a: Regret Minimization for Partially Observable Deep Reinforcement Learning (PHJ, KK, SL), pp. 2347–2356.
ICML-2018-Johnson0 #functional #generative #modelling: Composite Functional Gradient Learning of Generative Adversarial Models (RJ, TZ0), pp. 2376–2384.
ICML-2018-KalimerisSSW #using: Learning Diffusion using Hyperparameters (DK, YS, KS, UW), pp. 2425–2433.
ICML-2018-KalyanLKB #multi: Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations (AK, SL, AK, DB), pp. 2454–2463.
ICML-2018-KamnitsasCFWTRG #clustering: Semi-Supervised Learning via Compact Latent Space Clustering (KK, DCC, LLF, IW, RT, DR, BG, AC, AVN), pp. 2464–2473.
ICML-2018-KaplanisSC: Continual Reinforcement Learning with Complex Synapses (CK, MS, CC), pp. 2502–2511.
ICML-2018-KatharopoulosF: Not All Samples Are Created Equal: Deep Learning with Importance Sampling (AK, FF), pp. 2530–2539.
ICML-2018-KearnsNRW: Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness (MJK, SN, AR0, ZSW), pp. 2569–2577.
ICML-2018-KennamerKIS #classification #named: ContextNet: Deep learning for Star Galaxy Classification (NK, DK, ATI, FJSL), pp. 2587–2595.
ICML-2018-KhanNTLGS #performance #scalability: Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam (MEK, DN, VT, WL, YG, AS), pp. 2616–2625.
ICML-2018-KuleshovFE #nondeterminism #using: Accurate Uncertainties for Deep Learning Using Calibrated Regression (VK, NF, SE), pp. 2801–2809.
ICML-2018-LeeKCL #case study #game studies: Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling (KL, SAK, JC, SWL), pp. 2943–2952.
ICML-2018-LeeYH #multi #symmetry: Deep Asymmetric Multi-task Feature Learning (HL, EY, SJH), pp. 2962–2970.
ICML-2018-LehtinenMHLKAA #image #named: Noise2Noise: Learning Image Restoration without Clean Data (JL, JM, JH, SL, TK, MA, TA), pp. 2971–2980.
ICML-2018-LiangLNMFGGJS #abstraction #distributed #named: RLlib: Abstractions for Distributed Reinforcement Learning (EL, RL, RN, PM, RF, KG, JG, MIJ, IS), pp. 3059–3068.
ICML-2018-LiaoC18a #approach #matrix #random: The Dynamics of Learning: A Random Matrix Approach (ZL, RC), pp. 3078–3087.
ICML-2018-LiGD #bias #induction #network: Explicit Inductive Bias for Transfer Learning with Convolutional Networks (XL0, YG, FD), pp. 2830–2839.
ICML-2018-LiH #approach #network: An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks (QL, SH), pp. 2991–3000.
ICML-2018-LinC #distributed #multi #probability: Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods (JL, VC), pp. 3098–3107.
ICML-2018-LongLMD #named: PDE-Net: Learning PDEs from Data (ZL, YL, XM, BD0), pp. 3214–3222.
ICML-2018-LuoSZLZW: End-to-end Active Object Tracking via Reinforcement Learning (WL, PS, FZ, WL0, TZ0, YW), pp. 3292–3301.
ICML-2018-MaBB #comprehension #effectiveness #power of: The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning (SM, RB, MB), pp. 3331–3340.
ICML-2018-MadrasCPZ: Learning Adversarially Fair and Transferable Representations (DM, EC, TP, RSZ), pp. 3381–3390.
ICML-2018-MalikPFHRD #performance: An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning (DM, MP, JFF, DHM, SJR, ADD), pp. 3391–3399.
ICML-2018-MaWHZEXWB: Dimensionality-Driven Learning with Noisy Labels (XM, YW0, MEH, SZ0, SME, STX, SNRW, JB0), pp. 3361–3370.
ICML-2018-MeyersonM #multi #pseudo: Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing-and Back (EM, RM), pp. 3508–3517.
ICML-2018-MhamdiGR #distributed: The Hidden Vulnerability of Distributed Learning in Byzantium (EMEM, RG, SR), pp. 3518–3527.
ICML-2018-MishchenkoIMA #algorithm #distributed: A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning (KM, FI, JM, MRA), pp. 3584–3592.
ICML-2018-Nachum0TS #policy: Smoothed Action Value Functions for Learning Gaussian Policies (ON, MN0, GT, DS), pp. 3689–3697.
ICML-2018-NguyenSH #on the: On Learning Sparsely Used Dictionaries from Incomplete Samples (TVN, AS, CH), pp. 3766–3775.
ICML-2018-NickelK #geometry: Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry (MN, DK), pp. 3776–3785.
ICML-2018-OglicG #kernel: Learning in Reproducing Kernel Krein Spaces (DO, TG0), pp. 3856–3864.
ICML-2018-OhGSL #self: Self-Imitation Learning (JO, YG, SS, HL), pp. 3875–3884.
ICML-2018-OkunoHS #framework #multi #network #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-OsamaZS #locality #modelling #streaming: Learning Localized Spatio-Temporal Models From Streaming Data (MO, DZ, TBS), pp. 3924–3932.
ICML-2018-Oymak #network: Learning Compact Neural Networks with Regularization (SO), pp. 3963–3972.
ICML-2018-PaassenGMH #adaptation #distance #edit distance: Tree Edit Distance Learning via Adaptive Symbol Embeddings (BP, CG, AM, BH), pp. 3973–3982.
ICML-2018-PanFWNGN #difference #equation: Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control (YP, AmF, MW, SN, PG, DN), pp. 3983–3992.
ICML-2018-PanS #predict: Learning to Speed Up Structured Output Prediction (XP, VS), pp. 3993–4002.
ICML-2018-PanZD #analysis: Theoretical Analysis of Image-to-Image Translation with Adversarial Learning (XP, MZ, DD), pp. 4003–4012.
ICML-2018-ParascandoloKRS #independence: Learning Independent Causal Mechanisms (GP, NK, MRC, BS), pp. 4033–4041.
ICML-2018-PardoTLK: Time Limits in Reinforcement Learning (FP, AT, VL, PK), pp. 4042–4051.
ICML-2018-PearceBZN #approach #predict: High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach (TP, AB, MZ, AN), pp. 4072–4081.
ICML-2018-PretoriusKK #linear: Learning Dynamics of Linear Denoising Autoencoders (AP, SK, HK), pp. 4138–4147.
ICML-2018-PuDGWWZHC #generative #multi #named: JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets (YP, SD, ZG, WW, GW0, YZ, RH, LC), pp. 4148–4157.
ICML-2018-RaeDDL #parametricity #performance: Fast Parametric Learning with Activation Memorization (JWR, CD, PD, TPL), pp. 4225–4234.
ICML-2018-RaghuIAKLK #game studies #question: Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games? (MR, AI, JA, RK, QVL, JMK), pp. 4235–4243.
ICML-2018-RaileanuDSF #modelling #multi #using: Modeling Others using Oneself in Multi-Agent Reinforcement Learning (RR, ED, AS, RF), pp. 4254–4263.
ICML-2018-RashidSWFFW #multi #named: QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning (TR, MS, CSdW, GF, JNF, SW), pp. 4292–4301.
ICML-2018-RavuriMRV #generative #modelling: Learning Implicit Generative Models with the Method of Learned Moments (SVR, SM, MR, OV), pp. 4311–4320.
ICML-2018-RenZYU #robust: Learning to Reweight Examples for Robust Deep Learning (MR, WZ, BY, RU), pp. 4331–4340.
ICML-2018-RiedmillerHLNDW #game studies: Learning by Playing Solving Sparse Reward Tasks from Scratch (MAR, RH, TL, MN, JD, TVdW, VM, NH, JTS), pp. 4341–4350.
ICML-2018-RobertsERHE #music: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music (AR, JHE, CR, CH, DE), pp. 4361–4370.
ICML-2018-RosenfeldBGS #combinator: Learning to Optimize Combinatorial Functions (NR, EB, AG, YS), pp. 4371–4380.
ICML-2018-SahooLM #equation: Learning Equations for Extrapolation and Control (SSS, CHL, GM), pp. 4439–4447.
ICML-2018-SchmitJ: Learning with Abandonment (SS, RJ), pp. 4516–4524.
ICML-2018-SchwabKMMSK #multi: Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care (PS, EK, CM, DJM, CS, WK), pp. 4525–4534.
ICML-2018-Schwarz0LGTPH #framework #scalability: Progress & Compress: A scalable framework for continual learning (JS, WC0, JL, AGB, YWT, RP, RH), pp. 4535–4544.
ICML-2018-ShazeerS #adaptation #memory management #named #sublinear: Adafactor: Adaptive Learning Rates with Sublinear Memory Cost (NS, MS), pp. 4603–4611.
ICML-2018-SheldonWS #automation #difference #integer #modelling: Learning in Integer Latent Variable Models with Nested Automatic Differentiation (DS, KW, DS), pp. 4622–4630.
ICML-2018-ShenMZZQ #communication #convergence #distributed #performance #probability #towards: Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication (ZS, AM, TZ, PZ, HQ), pp. 4631–4640.
ICML-2018-ShiarlisWSWP #composition #named: TACO: Learning Task Decomposition via Temporal Alignment for Control (KS, MW, SS, SW, IP), pp. 4661–4670.
ICML-2018-SibliniMK #clustering #multi #performance #random: CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning (WS, FM, PK), pp. 4671–4680.
ICML-2018-SmithHP #policy: An Inference-Based Policy Gradient Method for Learning Options (MS, HvH, JP), pp. 4710–4719.
ICML-2018-SrinivasJALF #network: Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control (AS, AJ, PA, SL, CF), pp. 4739–4748.
ICML-2018-SroujiZS: Structured Control Nets for Deep Reinforcement Learning (MS, JZ, RS), pp. 4749–4758.
ICML-2018-SunZWZLG #composition #kernel #process: Differentiable Compositional Kernel Learning for Gaussian Processes (SS, GZ, CW, WZ, JL, RBG), pp. 4835–4844.
ICML-2018-SuW: Learning Low-Dimensional Temporal Representations (BS, YW), pp. 4768–4777.
ICML-2018-Talvitie: Learning the Reward Function for a Misspecified Model (ET), pp. 4845–4854.
ICML-2018-ThomasDB: Decoupling Gradient-Like Learning Rules from Representations (PST, CD, EB), pp. 4924–4932.
ICML-2018-TianZZ #named: CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions (KT, TZ, JZ), pp. 4933–4942.
ICML-2018-TirinzoniSPR: Importance Weighted Transfer of Samples in Reinforcement Learning (AT, AS, MP, MR), pp. 4943–4952.
ICML-2018-TrinhDLL #dependence: Learning Longer-term Dependencies in RNNs with Auxiliary Losses (THT, AMD, TL, QVL), pp. 4972–4981.
ICML-2018-TschannenKA #multi #named: StrassenNets: Deep Learning with a Multiplication Budget (MT, AK, AA), pp. 4992–5001.
ICML-2018-TuckerBGTGL: The Mirage of Action-Dependent Baselines in Reinforcement Learning (GT, SB, SG, RET, ZG, SL), pp. 5022–5031.
ICML-2018-TuR #difference #linear #polynomial: Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator (ST, BR), pp. 5012–5021.
ICML-2018-VermaMSKC: Programmatically Interpretable Reinforcement Learning (AV, VM, RS, PK, SC), pp. 5052–5061.
ICML-2018-VogelBC #optimisation #probability #similarity: A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization (RV, AB, SC), pp. 5062–5071.
ICML-2018-WagnerGKM #data type: Semi-Supervised Learning on Data Streams via Temporal Label Propagation (TW, SG, SPK, NM), pp. 5082–5091.
ICML-2018-WangGLWY #predict #towards: PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning (YW, ZG, ML, JW0, PSY), pp. 5110–5119.
ICML-2018-WangK #multi: Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations (XW, DK), pp. 5130–5138.
ICML-2018-WangSQ #modelling #multi #performance #scalability #visual notation: A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models (BW, AS, YQ), pp. 5148–5157.
ICML-2018-WeinshallCA #education #network: Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks (DW, GC, DA), pp. 5235–5243.
ICML-2018-WeiZHY: Transfer Learning via Learning to Transfer (YW, YZ, JH, QY), pp. 5072–5081.
ICML-2018-XiaTTQYL: Model-Level Dual Learning (YX, XT, FT, TQ, NY, TYL), pp. 5379–5388.
ICML-2018-XieWZX #analysis #distance #metric: Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis (PX, WW, YZ, EPX), pp. 5399–5408.
ICML-2018-XieZCC #adaptation #semantics: Learning Semantic Representations for Unsupervised Domain Adaptation (SX, ZZ, LC0, CC), pp. 5419–5428.
ICML-2018-XuCZ #process: Learning Registered Point Processes from Idiosyncratic Observations (HX, LC, HZ), pp. 5439–5448.
ICML-2018-XuLTSKJ #graph #network #representation: Representation Learning on Graphs with Jumping Knowledge Networks (KX, CL, YT, TS, KiK, SJ), pp. 5449–5458.
ICML-2018-XuLZP: Learning to Explore via Meta-Policy Gradient (TX, QL0, LZ, JP0), pp. 5459–5468.
ICML-2018-XuZFLB #semantics: A Semantic Loss Function for Deep Learning with Symbolic Knowledge (JX, ZZ, TF, YL, GVdB), pp. 5498–5507.
ICML-2018-YanCJ: Active Learning with Logged Data (SY, KC, TJ), pp. 5517–5526.
ICML-2018-YangKU #equivalence #graph: Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions (KDY, AK, CU), pp. 5537–5546.
ICML-2018-YangLLZZW #multi: Mean Field Multi-Agent Reinforcement Learning (YY, RL, ML, MZ, WZ0, JW0), pp. 5567–5576.
ICML-2018-YenKYHKR #composition #performance #scalability: Loss Decomposition for Fast Learning in Large Output Spaces (IEHY, SK, FXY, DNHR, SK, PR), pp. 5626–5635.
ICML-2018-YinCRB #distributed #statistics #towards: Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates (DY, YC0, KR, PLB), pp. 5636–5645.
ICML-2018-YonaR #approximate: Probably Approximately Metric-Fair Learning (GY, GNR), pp. 5666–5674.
ICML-2018-ZanetteB #bound #identification #problem: Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs (AZ, EB), pp. 5732–5740.
ICML-2018-ZhangLSD #dependence #fourier: Learning Long Term Dependencies via Fourier Recurrent Units (JZ, YL, ZS, ISD), pp. 5810–5818.
ICML-2018-ZhangYL0B #distributed #multi: Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents (KZ, ZY, HL0, TZ0, TB), pp. 5867–5876.
ICML-2018-Zhao0FYW #estimation #feature model: MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning (BZ, XS0, YF, YY0, YW), pp. 5907–5916.
ICML-2018-ZhaoDBZ #topic #word: Inter and Intra Topic Structure Learning with Word Embeddings (HZ, LD, WLB, MZ), pp. 5887–5896.
ICPR-2018-AfonsoPSP #classification #using: Improving Optimum- Path Forest Classification Using Unsupervised Manifold Learning (LCSA, DCGP, ANdS, JPP), pp. 560–565.
ICPR-2018-Aldana-LopezCZG #approach #network: Dynamic Learning Rate for Neural Networks: A Fixed-Time Stability Approach (RAL, LECM, JZ, DGG, AC), pp. 1378–1383.
ICPR-2018-BiFW #constraints #metric: Cayley- Klein Metric Learning with Shrinkage-Expansion Constraints (YB, BF, FW), pp. 43–48.
ICPR-2018-CaoCHP #identification #metric: Region-specific Metric Learning for Person Re-identification (MC, CC0, XH, SP), pp. 794–799.
ICPR-2018-CaoGWXW #detection: Gaze-Aided Eye Detection via Appearance Learning (LC, CG, KW, GX, FYW0), pp. 1965–1970.
ICPR-2018-CaoLL0JJC #detection #image: Deep Learning Based Bioresorbable Vascular Scaffolds Detection in IVOCT Images (YC, YL, JL, RZ0, QJ, JJ, YC), pp. 3778–3783.
ICPR-2018-CuiB00JH #graph #hybrid #kernel #network: A Deep Hybrid Graph Kernel Through Deep Learning Networks (LC, LB0, LR0, YW0, YJ0, ERH), pp. 1030–1035.
ICPR-2018-CuiZZH #multi #network #recognition #using: Multi-source Learning for Skeleton -based Action Recognition Using Deep LSTM Networks (RC, AZ, SZ, GH0), pp. 547–552.
ICPR-2018-DasRBP #classification #documentation #image #network: 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-Dey0GVLP #image #multi #retrieval #sketching #using: Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch (SD, AD0, SKG, EV, JL0, UP0), pp. 916–921.
ICPR-2018-DuCWP #distributed #named #representation: Zone2Vec: Distributed Representation Learning of Urban Zones (JD, YC, YW0, JP), pp. 880–885.
ICPR-2018-EleziTVP #network: Transductive Label Augmentation for Improved Deep Network Learning (IE, AT, SV, MP), pp. 1432–1437.
ICPR-2018-FuGA #detection #scalability: Simultaneous Context Feature Learning and Hashing for Large Scale Loop Closure Detection (ZF, YG, WA), pp. 1689–1694.
ICPR-2018-GaoDS: Discernibility Matrix-Based Ensemble Learning (SG, JD, HS), pp. 952–957.
ICPR-2018-GaolLH0W #automation #multi #predict: Automatic Facial Attractiveness Prediction by Deep Multi-Task Learning (LG, WL, ZH, DH0, YW), pp. 3592–3597.
ICPR-2018-GrelssonF #exponential #linear #network: Improved Learning in Convolutional Neural Networks with Shifted Exponential Linear Units (ShELUs) (BG, MF), pp. 517–522.
ICPR-2018-GuptaMSM #image #order #ranking #similarity: Learning an Order Preserving Image Similarity through Deep Ranking (NG, SM, SS, SM), pp. 1115–1120.
ICPR-2018-HailatK0: Deep Semi-Supervised Learning (ZH, AK, XwC0), pp. 2154–2159.
ICPR-2018-HanXW #generative #multi #network #representation: Learning Multi-view Generator Network for Shared Representation (TH0, XX, YNW), pp. 2062–2068.
ICPR-2018-HanXZL #composition #image #network: Learning Intrinsic Image Decomposition by Deep Neural Network with Perceptual Loss (GH, XX, WSZ, JL), pp. 91–96.
ICPR-2018-HaoDWT #fine-grained #named #representation #retrieval: DeepFirearm: Learning Discriminative Feature Representation for Fine-grained Firearm Retrieval (JH, JD0, WW0, TT), pp. 3335–3340.
ICPR-2018-HeGG #network: Structure Learning of Bayesian Networks by Finding the Optimal Ordering (CCH, XGG, ZgG), pp. 177–182.
ICPR-2018-HuangWDSL #image #lightweight: Lightweight Deep Residue Learning for Joint Color Image Demosaicking and Denoising (TH, FW, WD, GS, XL0), pp. 127–132.
ICPR-2018-HuKLFLZD #named #representation: FV-Net: learning a finger-vein feature representation based on a CNN (HH, WK, YL, YF, HL, JZ, FD), pp. 3489–3494.
ICPR-2018-JiangLSWZW #identification #similarity: Orientation-Guided Similarity Learning for Person Re-identification (NJ, JL, CS, YW, ZZ, WW), pp. 2056–2061.
ICPR-2018-LeiZH0HL #classification #multi #rank: Multi-classification of Parkinson's Disease via Sparse Low-Rank Learning (HL, YZ, ZH, FZ0, LH, BL), pp. 3268–3272.
ICPR-2018-LiCQWW #adaptation #network #semantics: Cross-domain Semantic Feature Learning via Adversarial Adaptation Networks (RL, WmC0, SQ, HSW, SW), pp. 37–42.
ICPR-2018-LiL #metric: Riemannian Metric Learning based on Curvature Flow (YL, RL), pp. 806–811.
ICPR-2018-LingLZG #classification #image #network: Semi-Supervised Learning via Convolutional Neural Network for Hyperspectral Image Classification (ZL, XL, WZ, SG), pp. 1–6.
ICPR-2018-LiuDWZWZ #classification #education #image: Teaching Squeeze-and-Excitation PyramidNet for Imbalanced Image Classification with GAN-based Curriculum Learning (JL, AD, CW0, HZ, NW0, BZ), pp. 2444–2449.
ICPR-2018-LiWK18a #framework #image #using: Infrared and Visible Image Fusion using a Deep Learning Framework (HL0, XJW, JK), pp. 2705–2710.
ICPR-2018-LuoZLW #clustering #graph #image: Graph Embedding-Based Ensemble Learning for Image Clustering (XL, LZ0, FL, BW), pp. 213–218.
ICPR-2018-LyuYCZZ #classification #detection: Learning Fixation Point Strategy for Object Detection and Classification (JL0, ZY, DC, YZ, HZ), pp. 2081–2086.
ICPR-2018-MaBCX0 #collaboration #visual notation: Learning Collaborative Model for Visual Tracking (DM, WB, YC, YX, XW0), pp. 2582–2587.
ICPR-2018-MadapanaW #gesture #recognition: Hard Zero Shot Learning for Gesture Recognition (NM, JPW), pp. 3574–3579.
ICPR-2018-MaierSSWSCF #network #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-ManessiR: Learning Combinations of Activation Functions (FM, AR), pp. 61–66.
ICPR-2018-NguyenNSADF #recognition: Meta Transfer Learning for Facial Emotion Recognition (DNT, KN0, SS, IA, DD, CF), pp. 3543–3548.
ICPR-2018-NguyenTL #data-driven #using: Are French Really That Different? Recognizing Europeans from Faces Using Data-Driven Learning (VDN, MT, JL), pp. 2729–2734.
ICPR-2018-NieLQZJ #algorithm #classification #incremental #multi: An Incremental Multi-view Active Learning Algorithm for PolSAR Data Classification (XN, YL, HQ, BZ0, ZPJ), pp. 2251–2255.
ICPR-2018-NiuHSC #named: SynRhythm: Learning a Deep Heart Rate Estimator from General to Specific (XN, HH, SS, XC), pp. 3580–3585.
ICPR-2018-NiuS0 #graph: Enhancing Knowledge Graph Completion with Positive Unlabeled Learning (JN, ZS, WZ0), pp. 296–301.
ICPR-2018-PangDWH: Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice (KP, MD, YW, TMH), pp. 2269–2276.
ICPR-2018-PeiFR #multi: Learning with Latent Label Hierarchy from Incomplete Multi-Label Data (YP, XZF, RR), pp. 2075–2080.
ICPR-2018-PengLMSL #detection #image #sequence #video: Driving Maneuver Detection via Sequence Learning from Vehicle Signals and Video Images (XP, RL, YLM, SS, YL), pp. 1265–1270.
ICPR-2018-RenZLLWY #rank #representation #robust #taxonomy: Robust Projective Low-Rank and Sparse Representation by Robust Dictionary Learning (JR, ZZ0, SL0, GL, MW0, SY), pp. 1851–1856.
ICPR-2018-RibaFLF #graph #message passing #network: Learning Graph Distances with Message Passing Neural Networks (PR, AF0, JL0, AF), pp. 2239–2244.
ICPR-2018-RoyT #higher-order #using: Learning to Learn Second-Order Back-Propagation for CNNs Using LSTMs (AR, ST), pp. 97–102.
ICPR-2018-RuedaF #process #recognition #representation: Learning Attribute Representation for Human Activity Recognition (FMR, GAF), pp. 523–528.
ICPR-2018-SahaVJ #named: Class2Str: End to End Latent Hierarchy Learning (SS, GV, CVJ), pp. 1000–1005.
ICPR-2018-SiddiquiV0 #approach #recognition: Face Recognition for Newborns, Toddlers, and Pre-School Children: A Deep Learning Approach (SS, MV, RS0), pp. 3156–3161.
ICPR-2018-SuiZYC #detection #framework #novel #recognition: A Novel Integrated Framework for Learning both Text Detection and Recognition (WS, QZ, JY, WC), pp. 2233–2238.
ICPR-2018-SunCWX #coordination #metric #online #parallel #rank: Online Low-Rank Metric Learning via Parallel Coordinate Descent Method (GS, YC, QW0, XX), pp. 207–212.
ICPR-2018-SunZJLWY #adaptation #robust #taxonomy: Robust Discriminative Projective Dictionary Pair Learning by Adaptive Representations (YS, ZZ0, WJ, GL, MW0, SY), pp. 621–626.
ICPR-2018-SunZWJ #behaviour #detection: Weak Supervised Learning Based Abnormal Behavior Detection (XS, SZ, SW, XYJ), pp. 1580–1585.
ICPR-2018-TayanovKS #classification #predict #using: Prediction-based classification using learning on Riemannian manifolds (VT, AK, CYS), pp. 591–596.
ICPR-2018-VinayavekhinCMA #comprehension #using #what: Focusing on What is Relevant: Time-Series Learning and Understanding using Attention (PV, SC, AM, DJA, GDM, DK, RT), pp. 2624–2629.
ICPR-2018-WangHJ #using: Focus on Scene Text Using Deep Reinforcement Learning (HW, SH, LJ), pp. 3759–3765.
ICPR-2018-WangSSL #metric: Nonlinear Metric Learning through Geodesic Interpolation within Lie Groups (ZW, BS, CDS, JL), pp. 898–903.
ICPR-2018-WangWCK #classification #image #metric #multi #set: Multiple Manifolds Metric Learning with Application to Image Set Classification (RW, XJW, KXC, JK), pp. 627–632.
ICPR-2018-WangWL #education #performance: Weakly- and Semi-supervised Faster R-CNN with Curriculum Learning (JW, XW, WL0), pp. 2416–2421.
ICPR-2018-WenWSY #adaptation #recognition #representation: Adaptive convolution local and global learning for class-level joint representation of face recognition with single sample per person (WW, XW0, LS, MY0), pp. 3537–3542.
ICPR-2018-WitmerB #classification #image #multi #using: Multi-label Classification of Stem Cell Microscopy Images Using Deep Learning (AW, BB), pp. 1408–1413.
ICPR-2018-WuLCW #multi #semantics: Learning a Hierarchical Latent Semantic Model for Multimedia Data (SHW, YSL, SHC, JCW), pp. 2995–3000.
ICPR-2018-WuYSZ #identification #ranking: Joint Semi-supervised Learning and Re-ranking for Vehicle Re-identification (FW, SY, JSS, BZ), pp. 278–283.
ICPR-2018-XuCG #modelling #multi #random #using: Common Random Subgraph Modeling Using Multiple Instance Learning (TX, DKYC, IG), pp. 1205–1210.
ICPR-2018-XuWK #correlation #representation: Non-negative Subspace Representation Learning Scheme for Correlation Filter Based Tracking (TX, XJW, JK), pp. 1888–1893.
ICPR-2018-XuZL18a #incremental #kernel #linear #online: A Linear Incremental Nyström Method for Online Kernel Learning (SX, XZ, SL), pp. 2256–2261.
ICPR-2018-YangDWL: Masked Label Learning for Optical Flow Regression (GY, ZD, SW, ZL), pp. 1139–1144.
ICPR-2018-YanWSLZ #image #network #using: Image Captioning using Adversarial Networks and Reinforcement Learning (SY, FW, JSS, WL, BZ), pp. 248–253.
ICPR-2018-Ye0 #classification #image #invariant: Rotational Invariant Discriminant Subspace Learning For Image Classification (QY, ZZ0), pp. 1217–1222.
ICPR-2018-YuanTLDZ #automation #multi #segmentation #using: Fully Automatic Segmentation of the Left Ventricle Using Multi-Scale Fusion Learning (TY, QT, XL, XD, JZ), pp. 3838–3843.
ICPR-2018-YuanWXZ #empirical #estimation #multi: Multiple- Instance Learning with Empirical Estimation Guided Instance Selection (LY, XW, HX, LZ), pp. 770–775.
ICPR-2018-YuanZLQ0S #adaptation #canonical #correlation #parallel #recognition: Learning Parallel Canonical Correlations for Scale-Adaptive Low Resolution Face Recognition (YY, ZZ, YL0, JPQ, BL0, XBS), pp. 922–927.
ICPR-2018-ZengZQQB0 #image: Single Image Super-Resolution With Learning Iteratively Non-Linear Mapping Between Low- and High-Resolution Sparse Representations (KZ, HZ, YQ, XQ, LB, ZC0), pp. 507–512.
ICPR-2018-ZhangJCXP #approach #graph #kernel #network: Kernel-Weighted Graph Convolutional Network: A Deep Learning Approach for Traffic Forecasting (QZ, QJ, JC, SX, CP), pp. 1018–1023.
ICPR-2018-ZhangWG #adaptation #multi #representation: Adaptive Latent Representation for Multi-view Subspace Learning (YZ, XW, XG), pp. 1229–1234.
ICPR-2018-ZhangWGWXL #detection #effectiveness #network: An Effective Deep Learning Based Scheme for Network Intrusion Detection (HZ, CQW, SG, ZW, YX, YL), pp. 682–687.
ICPR-2018-ZhaoPL0DWQ #locality #semantics #topic #using: Learning Topics Using Semantic Locality (ZZ, KP, SL, ZL0, CD, YW, QQ), pp. 3710–3715.
ICPR-2018-ZhouL0LL #estimation: Scale and Orientation Aware EPI-Patch Learning for Light Field Depth Estimation (WZ, LL, HZ, AL, LL), pp. 2362–2367.
ICPR-2018-ZhouMMB #detection: Learning Training Samples for Occlusion Edge Detection and Its Application in Depth Ordering Inference (YZ0, JM, AM, XB), pp. 541–546.
ICPR-2018-ZhouWD #online #realtime #robust: Online Learning of Spatial-Temporal Convolution Response for Robust Real-Time Tracking (JZ, RW, JD), pp. 1821–1826.
ICPR-2018-Zhuang0CW #classification #multi: Multi-task Learning of Cascaded CNN for Facial Attribute Classification (NZ, YY0, SC, HW), pp. 2069–2074.
ICPR-2018-ZhuangTYMZJX #image #named #representation #segmentation #semantics: RelationNet: Learning Deep-Aligned Representation for Semantic Image Segmentation (YZ, LT, FY, CM, ZZ, HJ, XX), pp. 1506–1511.
ICPR-2018-ZhuX #approximate #graph #scalability: Scalable Semi-Supervised Learning by Graph Construction with Approximate Anchors Embedding (HZ, MX), pp. 1331–1336.
ICPR-2018-ZhuZZ: Learning Evasion Strategy in Pursuit-Evasion by Deep Q-network (JZ, WZ, ZZ), pp. 67–72.
ICPR-2018-ZhuZZ18b #recognition #representation: End-to-end Video-level Representation Learning for Action Recognition (JZ, ZZ, WZ), pp. 645–650.
KDD-2018-0009QG0H #realtime: Deep Reinforcement Learning for Sponsored Search Real-time Bidding (JZ0, GQ, ZG, WZ0, XH), pp. 1021–1030.
KDD-2018-BaiZEV #representation: Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time (TB, SZ, BLE, SV), pp. 43–51.
KDD-2018-CaiWGSJ #multi: Deep Adversarial Learning for Multi-Modality Missing Data Completion (LC, ZW, HG, DS, SJ), pp. 1158–1166.
KDD-2018-CardosoDV #personalisation #recommendation #semistructured data #towards: Product Characterisation towards Personalisation: Learning Attributes from Unstructured Data to Recommend Fashion Products (ÂC, FD, SV), pp. 80–89.
KDD-2018-Chen0DTHT #online #recommendation: Stabilizing Reinforcement Learning in Dynamic Environment with Application to Online Recommendation (SYC, YY0, QD, JT, HKH, HHT), pp. 1187–1196.
KDD-2018-DasSCHLCKC #named #performance #using: SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression (ND, MS, STC, FH, SL, LC, MEK, DHC), pp. 196–204.
KDD-2018-DiPSC #morphism: Transfer Learning via Feature Isomorphism Discovery (SD, JP, YS, LC), pp. 1301–1309.
KDD-2018-DonnatZHL: Learning Structural Node Embeddings via Diffusion Wavelets (CD, MZ, DH, JL), pp. 1320–1329.
KDD-2018-FoxAJPW #multi #predict: Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories (IF, LA, MJ, RPB, JW), pp. 1387–1395.
KDD-2018-FuWHW #approximate #fault #reduction #scalability: Scalable Active Learning by Approximated Error Reduction (WF, MW, SH, XW0), pp. 1396–1405.
KDD-2018-GohSVH #predict #rule-based #using: Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction (GBG, CS, AV, NOH), pp. 302–310.
KDD-2018-GorovitsGPB #community #named: LARC: Learning Activity-Regularized Overlapping Communities Across Time (AG, EG, EEP, PB), pp. 1465–1474.
KDD-2018-GuYCH #algorithm #incremental: New Incremental Learning Algorithm for Semi-Supervised Support Vector Machine (BG, XTY, SC, HH), pp. 1475–1484.
KDD-2018-HanSSZ #collaboration #multi #semistructured data: Multi-label Learning with Highly Incomplete Data via Collaborative Embedding (YH, GS, YS, XZ0), pp. 1494–1503.
KDD-2018-HongCL #kernel: Disturbance Grassmann Kernels for Subspace-Based Learning (JH, HC, FL), pp. 1521–1530.
KDD-2018-HuaiMLSSZ #metric #probability: Metric Learning from Probabilistic Labels (MH, CM, YL, QS, LS, AZ), pp. 1541–1550.
KDD-2018-HuDZ0X #analysis #e-commerce #formal method #rank: Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application (YH, QD, AZ, YY0, YX), pp. 368–377.
KDD-2018-Janakiraman #multi #safety #using: Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning (VMJ), pp. 406–415.
KDD-2018-JeongJ #multi: Variable Selection and Task Grouping for Multi-Task Learning (JYJ, CHJ), pp. 1589–1598.
KDD-2018-KumagaiI #bound: Learning Dynamics of Decision Boundaries without Additional Labeled Data (AK, TI), pp. 1627–1636.
KDD-2018-Le0V #memory management: Dual Memory Neural Computer for Asynchronous Two-view Sequential Learning (HL, TT0, SV), pp. 1637–1645.
KDD-2018-LeeAVN #collaboration #comprehension #metric #video: Collaborative Deep Metric Learning for Video Understanding (JL, SAEH, BV, AN), pp. 481–490.
KDD-2018-LiaoZWMCYGW #predict #sequence: Deep Sequence Learning with Auxiliary Information for Traffic Prediction (BL, JZ, CW0, DM, TC, SY, YG, FW), pp. 537–546.
KDD-2018-LiFWSYL #estimation #multi #representation: Multi-task Representation Learning for Travel Time Estimation (YL, KF, ZW, CS, JY, YL0), pp. 1695–1704.
KDD-2018-LinZXZ #multi #performance #scalability: Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning (KL, RZ, ZX, JZ), pp. 1774–1783.
KDD-2018-LiuZC #metric #performance: Efficient Similar Region Search with Deep Metric Learning (YL, KZ0, GC), pp. 1850–1859.
KDD-2018-LiY #classification #network #policy: Learning Adversarial Networks for Semi-Supervised Text Classification via Policy Gradient (YL, JY), pp. 1715–1723.
KDD-2018-LiZLHMC #behaviour #recommendation: Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors (ZL, HZ, QL0, ZH, TM, EC), pp. 1734–1743.
KDD-2018-LiZY #approach: Dynamic Bike Reposition: A Spatio-Temporal Reinforcement Learning Approach (YL, YZ, QY), pp. 1724–1733.
KDD-2018-LuJZDZW #named #semantics #visual notation: R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering (PL, LJ, WZ0, ND, MZ0, JW), pp. 1880–1889.
KDD-2018-LuoCTSLCY #information management #invariant #named #network: TINET: Learning Invariant Networks via Knowledge Transfer (CL, ZC, LAT, AS, ZL, HC, JY), pp. 1890–1899.
KDD-2018-MaZYCHC #modelling #multi: Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts (JM, ZZ, XY, JC, LH, EHC), pp. 1930–1939.
KDD-2018-NieHL #multi: Calibrated Multi-Task Learning (FN, ZH, XL), pp. 2012–2021.
KDD-2018-NiOLLOZS #e-commerce #multi: Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks (YN, DO, SL, XL, WO, AZ, LS), pp. 596–605.
KDD-2018-OshriHACDWBLE #assessment #framework #quality #using: Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning (BO, AH, PA, XC, PD, JW, MB, DBL, SE), pp. 616–625.
KDD-2018-PangCCL #detection #random: Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection (GP, LC, LC, HL), pp. 2041–2050.
KDD-2018-QiuTMDW0 #named #predict #social: DeepInf: Social Influence Prediction with Deep Learning (JQ, JT, HM, YD, KW, JT0), pp. 2110–2119.
KDD-2018-RaoTL #comprehension #framework #multi #network #platform #query: Multi-Task Learning with Neural Networks for Voice Query Understanding on an Entertainment Platform (JR, FT, JL), pp. 636–645.
KDD-2018-SamelM: Active Deep Learning to Tune Down the Noise in Labels (KS, XM), pp. 685–694.
KDD-2018-SatoNHMHAM #detection: Managing Computer-Assisted Detection System Based on Transfer Learning with Negative Transfer Inhibition (IS, YN, SH, SM, NH, OA, YM), pp. 695–704.
KDD-2018-ShiZGZ0 #network: Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks (YS, QZ, FG, CZ0, JH0), pp. 2190–2199.
KDD-2018-SureshGG #multi: Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU (HS, JJG, JVG), pp. 802–810.
KDD-2018-TangW #modelling #performance #ranking #recommendation: Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System (JT, KW), pp. 2289–2298.
KDD-2018-Teh #big data #on the #problem: On Big Data Learning for Small Data Problems (YWT), p. 3.
KDD-2018-VandalKDGNG #nondeterminism: Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning (TV, EK, JGD, SG, RRN, ARG), pp. 2377–2386.
KDD-2018-WangFY: Learning to Estimate the Travel Time (ZW, KF, JY), pp. 858–866.
KDD-2018-WangFZWZA #analysis #behaviour #how #representation: You Are How You Drive: Peer and Temporal-Aware Representation Learning for Driving Behavior Analysis (PW, YF, JZ, PW, YZ, CCA), pp. 2457–2466.
KDD-2018-WangJZEC #behaviour #multi: Multi-Type Itemset Embedding for Learning Behavior Success (DW, MJ0, QZ, ZE, NVC), pp. 2397–2406.
KDD-2018-WangOWW #modelling: Learning Credible Models (JW, JO, HW, JW), pp. 2417–2426.
KDD-2018-WangZ #problem #towards: Towards Mitigating the Class-Imbalance Problem for Partial Label Learning (JW, MLZ), pp. 2427–2436.
KDD-2018-WangZBZCY #mobile #performance #privacy: Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud (JW0, JZ, WB, XZ, BC, PSY), pp. 2407–2416.
KDD-2018-WangZHZ #network #recommendation: Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation (LW, WZ0, XH, HZ), pp. 2447–2456.
KDD-2018-WeiZYL #approach #named: IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control (HW, GZ, HY, ZL), pp. 2496–2505.
KDD-2018-WuYC #realtime: Deep Censored Learning of the Winning Price in the Real Time Bidding (WCHW, MYY, MSC), pp. 2526–2535.
KDD-2018-WuYYZ #process: Decoupled Learning for Factorial Marked Temporal Point Processes (WW, JY, XY, HZ), pp. 2516–2525.
KDD-2018-XuLDH #metric #robust #using: New Robust Metric Learning Model Using Maximum Correntropy Criterion (JX0, LL, CD, HH), pp. 2555–2564.
KDD-2018-XuLGZLNLBY #approach #on-demand #order #platform #scalability: Large-Scale Order Dispatch in On-Demand Ride-Hailing Platforms: A Learning and Planning Approach (ZX, ZL, QG, DZ, QL, JN, CL, WB, JY), pp. 905–913.
KDD-2018-YangZTWCH #case study #contest #image #recognition: Deep Learning for Practical Image Recognition: Case Study on Kaggle Competitions (XY, ZZ, SGT, LW, VC0, SCHH), pp. 923–931.
KDD-2018-YoshidaTK #distance #metric: Safe Triplet Screening for Distance Metric Learning (TY, IT, MK), pp. 2653–2662.
KDD-2018-YuanZY #approach #named #predict: Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Temporal Data (ZY, XZ, TY), pp. 984–992.
KDD-2018-YuZCASZCW #network: Learning Deep Network Representations with Adversarially Regularized Autoencoders (WY, CZ, WC, CCA, DS, BZ, HC, WW0), pp. 2663–2671.
KDD-2018-ZangC0 #empirical: Learning and Interpreting Complex Distributions in Empirical Data (CZ, PC0, WZ0), pp. 2682–2691.
KDD-2018-ZhangWLTYY #matrix #self: Discrete Ranking-based Matrix Factorization with Self-Paced Learning (YZ0, HW, DL, IWT, HY, GY), pp. 2758–2767.
KDD-2018-ZhangZCMHWT #adaptation #online #symmetry: Online Adaptive Asymmetric Active Learning for Budgeted Imbalanced Data (YZ0, PZ, JC, WM, JH, QW, MT), pp. 2768–2777.
KDD-2018-ZhaoLSY #e-commerce #representation: Learning and Transferring IDs Representation in E-commerce (KZ, YL, ZS, CY), pp. 1031–1039.
KDD-2018-ZhaoZDXTY #feedback #recommendation: Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning (XZ, LZ, ZD, LX, JT, DY), pp. 1040–1048.
KDD-2018-ZhuLZLHLG #recommendation: Learning Tree-based Deep Model for Recommender Systems (HZ, XL, PZ, GL, JH, HL, KG), pp. 1079–1088.
ECOOP-2018-ChenHZHK0 #execution #program transformation #symbolic computation: Learning to Accelerate Symbolic Execution via Code Transformation (JC0, WH, LZ, DH, SK, LZ0), p. 27.
Onward-2018-RinardSM #source code: Active learning for inference and regeneration of computer programs that store and retrieve data (MCR, JS0, VM), pp. 12–28.
OOPSLA-2018-EzudheenND0M #contract #invariant: Horn-ICE learning for synthesizing invariants and contracts (PE, DN, DD, PG0, PM), p. 25.
OOPSLA-2018-PradelS #approach #debugging #detection #named: DeepBugs: a learning approach to name-based bug detection (MP, KS), p. 25.
PLDI-2018-Bastani0AL #points-to #specification: Active learning of points-to specifications (OB, RS0, AA, PL), pp. 678–692.
PLDI-2018-FengMBD #synthesis #using: Program synthesis using conflict-driven learning (YF, RM, OB, ID), pp. 420–435.
SAS-2018-PrabhuMV #behaviour #proving #safety: Efficiently Learning Safety Proofs from Appearance as well as Behaviours (SP, KM, RV), pp. 326–343.
ASE-2018-ChaLO #online #testing: Template-guided concolic testing via online learning (SC, SL, HO), pp. 408–418.
ASE-2018-GaoYFJS #named #platform #semantics: VulSeeker: a semantic learning based vulnerability seeker for cross-platform binary (JG, XY, YF, YJ0, JS), pp. 896–899.
ASE-2018-HabibP #documentation #graph #thread #using: Is this class thread-safe? inferring documentation using graph-based learning (AH, MP), pp. 41–52.
ASE-2018-HanYL #debugging #named #performance: PerfLearner: learning from bug reports to understand and generate performance test frames (XH, TY, DL0), pp. 17–28.
ASE-2018-LiuXZ #detection: Deep learning based feature envy detection (HL, ZX, YZ), pp. 385–396.
ASE-2018-MaJZSXLCSLLZW #multi #named #testing: DeepGauge: multi-granularity testing criteria for deep learning systems (LM0, FJX, FZ, JS, MX, BL0, CC, TS, LL0, YL0, JZ, YW), pp. 120–131.
ASE-2018-TufanoWBPWP #empirical: An empirical investigation into learning bug-fixing patches in the wild via neural machine translation (MT, CW, GB, MDP, MW, DP), pp. 832–837.
ASE-2018-WanZYXY0Y #automation #source code #summary: Improving automatic source code summarization via deep reinforcement learning (YW, ZZ, MY0, GX, HY, JW0, PSY), pp. 397–407.
ESEC-FSE-2018-GaoYFJSS #named #semantics: VulSeeker-pro: enhanced semantic learning based binary vulnerability seeker with emulation (JG, XY, YF, YJ0, HS, JS), pp. 803–808.
ESEC-FSE-2018-GuoJZCS #difference #fuzzing #named #testing: DLFuzz: differential fuzzing testing of deep learning systems (JG, YJ0, YZ, QC, JS), pp. 739–743.
ESEC-FSE-2018-HellendoornBBA #type inference: Deep learning type inference (VJH, CB, ETB, MA), pp. 152–162.
ESEC-FSE-2018-JamshidiVKS #configuration management #modelling #performance: Learning to sample: exploiting similarities across environments to learn performance models for configurable systems (PJ, MV, CK, NS), pp. 71–82.
ESEC-FSE-2018-Meijer #concept #framework #programming language: Behind every great deep learning framework is an even greater programming languages concept (keynote) (EM0), p. 1.
ESEC-FSE-2018-ZhaoH #functional #named #similarity: DeepSim: deep learning code functional similarity (GZ, JH0), pp. 141–151.
ICSE-2018-PhanNTTNN #api #online #statistics: Statistical learning of API fully qualified names in code snippets of online forums (HP, HAN, NMT, LHT, ATN0, TNN), pp. 632–642.
ASPLOS-2018-MishraILH #energy #latency #named #predict: CALOREE: Learning Control for Predictable Latency and Low Energy (NM, CI, JDL, HH), pp. 184–198.
CASE-2018-BanerjeeRP #towards: A Step Toward Learning to Control Tens of Optically Actuated Microrobots in Three Dimensions (AGB, KR, BP), pp. 1460–1465.
CASE-2018-ChuBIICS #multi #online #power management #using: Plug-and-Play Power Management Control of All-Electric Vehicles Using Multi-Agent System and On-line Gaussian Learning (KCC, GB, MI, AI, CC, KS), pp. 1599–1604.
CASE-2018-FarooquiFF #automation #modelling #simulation #towards: Towards Automatic Learning of Discrete-Event Models from Simulations (AF, PF, MF), pp. 857–862.
CASE-2018-HuaH #concept #induction #logic programming #semantics: Concept Learning in AutomationML with Formal Semantics and Inductive Logic Programming (YH, BH), pp. 1542–1547.
CASE-2018-JiKPFG #2d: Learning 2D Surgical Camera Motion From Demonstrations (JJJ, SK, VP, DF, KG), pp. 35–42.
CASE-2018-LeeLFG #constraints #estimation: Constraint Estimation and Derivative-Free Recovery for Robot Learning from Demonstrations (JL, ML, RF, KG), pp. 270–277.
CASE-2018-NagahamaTYYYI: A Learning Method for a Daily Assistive Robot for Opening and Closing Doors Based on Simple Instructions (KN, KT, HY, KY, TY, MI), pp. 599–605.
CASE-2018-NeumannNKM #classification: Material Classification through Knocking and Grasping by Learning of Structure-Borne Sound under Changing Acoustic Conditions (MN, KN, IK, ZCM), pp. 1269–1275.
CASE-2018-ParkHGS #process: Robot Model Learning with Gaussian Process Mixture Model (SP, YH, CFG, KS), pp. 1263–1268.
CASE-2018-RenWLG #behaviour #online #video: Learning Traffic Behaviors by Extracting Vehicle Trajectories from Online Video Streams (XR, DW, ML, KG), pp. 1276–1283.
CASE-2018-SeichterESG #detection #how: How to Improve Deep Learning based Pavement Distress Detection while Minimizing Human Effort (DS, ME0, RS, HMG), pp. 63–70.
CASE-2018-TanCPP #analysis #automation #design #visual notation: Transfer Learning with PipNet: For Automated Visual Analysis of Piping Design (WCT, IMC, DP, SJP), pp. 1296–1301.
CASE-2018-TanGCC #analysis #automation: Learning with Corrosion Feature: For Automated Quantitative Risk Analysis of Corrosion Mechanism (WCT, PCG, KHC, IMC), pp. 1290–1295.
CASE-2018-TsengWCMSVVCOG #automation #image #precise #towards: Towards Automating Precision Irrigation: Deep Learning to Infer Local Soil Moisture Conditions from Synthetic Aerial Agricultural Images (DT, DWLW, CC, LM, WS, JV, SV, SC, JAO, KG), pp. 284–291.
CASE-2018-WangKZY #concept #data type #detection #multi: A Multiscale Concept Drift Detection Method for Learning from Data Streams (XW, QK, MZ, SY), pp. 786–790.
CASE-2018-YangZCTK: Intelligent Diagnosis of Forging Die based on Deep Learning (HCY, CHZ, YZC, CMT, YCK), pp. 199–204.
ESOP-2018-MertenBS #algorithm #complexity #distributed #game studies: Verified Learning Without Regret - From Algorithmic Game Theory to Distributed Systems with Mechanized Complexity Guarantees (SM, AB, GS0), pp. 561–588.
CAV-2018-DreossiJS #semantics: Semantic Adversarial Deep Learning (TD, SJ, SAS), pp. 3–26.
CAV-2018-KelmendiKKW #algorithm #game studies #probability: Value Iteration for Simple Stochastic Games: Stopping Criterion and Learning Algorithm (EK, JK, JK, MW), pp. 623–642.
CAV-2018-SinghPV #performance #program analysis: Fast Numerical Program Analysis with Reinforcement Learning (GS, MP, MTV), pp. 211–229.
CAV-2018-WangADM #abstraction #synthesis: Learning Abstractions for Program Synthesis (XW0, GA, ID, KLM), pp. 407–426.
CAV-2018-ZhouL: Safety-Aware Apprenticeship Learning (WZ, WL), pp. 662–680.
ICTSS-2018-SalvaBL #component #data analysis #modelling: Combining Model Learning and Data Analysis to Generate Models of Component-Based Systems (SS, EB, PL), pp. 142–148.
IJCAR-2018-PiotrowskiU #feedback #named: ATPboost: Learning Premise Selection in Binary Setting with ATP Feedback (BP, JU), pp. 566–574.
VMCAI-2018-LiTZS #automaton: Learning to Complement Büchi Automata (YL0, AT, LZ0, SS), pp. 313–335.
JCDL-2017-WeihsE #metric #predict: Learning to Predict Citation-Based Impact Measures (LW, OE), pp. 49–58.
JCDL-2017-YangHHOZKG #identification #library #using: Smart Library: Identifying Books on Library Shelves Using Supervised Deep Learning for Scene Text Reading (XY, DH, WH, AO, ZZ, DK, CLG), pp. 245–248.
CSEET-2017-BinderNRM #challenge #development #education #mobile: Challenge Based Learning Applied to Mobile Software Development Teaching (FVB, MN, SSR, AM), pp. 57–64.
CSEET-2017-LeildeR #assessment #process: Does Process Assessment Drive Process Learning? The Case of a Bachelor Capstone Project (VL, VR), pp. 197–201.
EDM-2017-AgrawalNM #student: Grouping Students for Maximizing Learning from Peers (RA, SN, NMM).
EDM-2017-BaoCH #multi #on the #online: On the Prevalence of Multiple-Account Cheating in Massive Open Online Learning (YB, GC, CH).
EDM-2017-BeckCB #data mining #education #mining: Workshop proposal: deep learning for educational data mining (JB, MC, RSB).
EDM-2017-CaiEDPGS #analysis #chat #collaboration #modelling #network #topic: Epistemic Network Analysis and Topic Modeling for Chat Data from Collaborative Learning Environment (ZC, BRE, ND, JWP, ACG, DWS).
EDM-2017-DongB #behaviour #modelling #student: An Extended Learner Modeling Method to Assess Students' Learning Behaviors (YD, GB).
EDM-2017-EkambaramMDKSN #physics: Tell Me More: Digital Eyes to the Physical World for Early Childhood Learning (VE, RSM, PD, RK, AKS, SVN).
EDM-2017-FangNPXGH #online #persistent: Online Learning Persistence and Academic Achievement (YF, BN, PIPJ, YX, ACG, XH).
EDM-2017-GrawemeyerWSHMP #graph #modelling #student #using: Using Graph-based Modelling to explore changes in students' affective states during exploratory learning tasks (BG, AW, SGS, WH, MM, AP).
EDM-2017-HongB #predict #using: A Prediction and Early Alert Model Using Learning Management System Data and Grounded in Learning Science Theory (WJH, MLB).
EDM-2017-LalleCATM #on the #self #student: On the Influence on Learning of Student Compliance with Prompts Fostering Self-Regulated Learning (SL, CC, RA, MT, NM).
EDM-2017-LiuK #automation #data-driven: Closing the loop: Automated data-driven cognitive model discoveries lead to improved instruction and learning (RL0, KRK).
EDM-2017-MaM #composition: Intelligent Composition of Test Papers based on MOOC Learning Data (LM, YM).
EDM-2017-NamFC #predict #semantics #word: Predicting Short- and Long-Term Vocabulary Learning via Semantic Features of Partial Word Knowledge (SN, GAF, KCT).
EDM-2017-RomeroEGGM #automation #classification #towards: Towards Automatic Classification of Learning Objects: Reducing the Number of Used Features (CR, PGE, EG, AZG, VHM).
EDM-2017-ShiPG #analysis #performance #using: Using an Additive Factor Model and Performance Factor Analysis to Assess Learning Gains in a Tutoring System to Help Adults with Reading Difficulties (GS, PIPJ, ACG).
EDM-2017-SuprajaHTK #automation #towards: Toward the Automatic Labeling of Course Questions for Ensuring their Alignment with Learning Outcomes (SS, KH, ST, AWHK).
EDM-2017-ThanasuanCW #mining #student: Emerging Patterns in Student's Learning Attributes through Text Mining (KT, WC, CW).
EDM-2017-WangSLP #programming #student #using: Learning to Represent Student Knowledge on Programming Exercises Using Deep Learning (LW, AS, LL, CP).
EDM-2017-WatersGLB: Short-Answer Responses to STEM Exercises: Measuring Response Validity and Its Impact on Learning (AEW, PG, ASL, RGB).
EDM-2017-XieMSEBH #adaptation #online #predict #student: Student Learning Strategies to Predict Success in an Online Adaptive Mathematics Tutoring System (JX, SM, KTS, AE, RSB, XH).
EDM-2017-ZhouWLC #policy #towards: Towards Closing the Loop: Bridging Machine-induced Pedagogical Policies to Learning Theories (GZ, JW, CL, MC).
ICPC-2017-LamNNN #debugging #information retrieval #locality: Bug localization with combination of deep learning and information retrieval (ANL, ATN0, HAN, TNN), pp. 218–229.
ICSME-2017-DeshmukhMPSD #debugging #retrieval #towards #using: Towards Accurate Duplicate Bug Retrieval Using Deep Learning Techniques (JD, KMA, SP, SS, ND), pp. 115–124.
ICSME-2017-HanLXLF #predict #using: Learning to Predict Severity of Software Vulnerability Using Only Vulnerability Description (ZH, XL0, ZX, HL, ZF0), pp. 125–136.
ICSME-2017-LiJZZ #fault #kernel #multi #predict: Heterogeneous Defect Prediction Through Multiple Kernel Learning and Ensemble Learning (ZL0, XYJ, XZ, HZ0), pp. 91–102.
ICSME-2017-VerwerH #automaton #named: flexfringe: A Passive Automaton Learning Package (SV, CAH), pp. 638–642.
ICSME-2017-WiemanALVD #case study #experience #scalability: An Experience Report on Applying Passive Learning in a Large-Scale Payment Company (RW, MFA, WL, SV, AvD), pp. 564–573.
SANER-2017-GoerFM #execution #named: scat: Learning from a single execution of a binary (FdG, CF, LM), pp. 492–496.
SANER-2017-SharmaTSLY #developer #twitter: Harnessing Twitter to support serendipitous learning of developers (AS0, YT0, AS, DL0, AFY), pp. 387–391.
IFM-2017-SilvettiPB #approach #black box #cyber-physical: An Active Learning Approach to the Falsification of Black Box Cyber-Physical Systems (SS, AP, LB), pp. 3–17.
SEFM-2017-CabodiCPPV #bound #model checking: Interpolation-Based Learning as a Mean to Speed-Up Bounded Model Checking (Short Paper) (GC, PC, MP, PP, DV), pp. 382–387.
AIIDE-2017-BarrigaSB #game studies #realtime: Combining Strategic Learning with Tactical Search in Real-Time Strategy Games (NAB, MS, MB), pp. 9–15.
AIIDE-2017-CampbellV: Learning Combat in NetHack (JC, CV), pp. 16–22.
AIIDE-2017-SigurdsonB #algorithm #heuristic #realtime: Deep Learning for Real-Time Heuristic Search Algorithm Selection (DS, VB), pp. 108–114.
CHI-PLAY-2017-ArroyoMCOHR #game studies #multi #smarttech: Wearable Learning: Multiplayer Embodied Games for Math (IA, MM, JC, EO, TH, MMTR), pp. 205–216.
CHI-PLAY-2017-JohansonGM #3d #game studies #navigation #performance: The Effects of Navigation Assistance on Spatial Learning and Performance in a 3D Game (CJ, CG, RLM), pp. 341–353.
CHI-PLAY-2017-ScozziIL #approach #design #game studies: A Mixed Method Approach for Evaluating and Improving the Design of Learning in Puzzle Games (MVS, II, CL), pp. 217–228.
CIG-2017-IlhanE #game studies #monte carlo #video: Monte Carlo tree search with temporal-difference learning for general video game playing (EI, ASEU), pp. 317–324.
CIG-2017-JustesenR #using: Learning macromanagement in starcraft from replays using deep learning (NJ, SR), pp. 162–169.
CIG-2017-MinK #game studies #using #visual notation: Learning to play visual doom using model-free episodic control (BJM, KJK), pp. 223–225.
CIG-2017-NguyenRGM #automation #network: Automated learning of hierarchical task networks for controlling minecraft agents (CN, NR, SG, HMA), pp. 226–231.
CIG-2017-OonishiI #game studies #using: Improving generalization ability in a puzzle game using reinforcement learning (HO, HI), pp. 232–239.
CIG-2017-OsbornSM #automation #design #game studies: Automated game design learning (JCO, AS, MM), pp. 240–247.
CIG-2017-PhucNK #behaviour #statistics #using: Learning human-like behaviors using neuroevolution with statistical penalties (LHP, KN, KI), pp. 207–214.
CIG-2017-PoulsenTFR #named #visual notation: DLNE: A hybridization of deep learning and neuroevolution for visual control (APP, MT, MHF, SR), pp. 256–263.
CIG-2017-ZhangB #policy: Improving hearthstone AI by learning high-level rollout policies and bucketing chance node events (SZ, MB), pp. 309–316.
DiGRA-2017-Loban #game studies #video: Digitising Diplomacy: Grand Strategy Video Games as an Introductory Tool for Learning Diplomacy and International Relations (RL).
DiGRA-2017-TyackW #adaptation #design #game studies: Adapting Epic Theatre Principles for the Design of Games for Learning (AT, PW).
FDG-2017-BauerBP #architecture #design #game studies #problem: Dragon architect: open design problems for guided learning in a creative computational thinking sandbox game (AB0, EB, ZP), p. 6.
FDG-2017-KaravolosLY #game studies #multi: Learning the patterns of balance in a multi-player shooter game (DK, AL, GNY), p. 10.
FDG-2017-LaffeyGSLSGKWW #game studies: Mission HydroSci: a progress report on a transformational role playing game for science learning (JML, JG, JS, SL, TDS, SPG, SMK, EW, AJW), p. 4.
FDG-2017-Valls-VargasZO #game studies #generative #grammarware #graph grammar #parallel #programming: Graph grammar-based controllable generation of puzzles for a learning game about parallel programming (JVV, JZ, SO), p. 10.
ICGJ-2017-PollockMY #development #game studies: Brain jam: STEAM learning through neuroscience-themed game development (IP, JM, BY), pp. 15–21.
VS-Games-2017-BlomeDRBM #artificial reality: VReanimate - Non-verbal guidance and learning in virtual reality (TB, AD, SR, KB, SvM), pp. 23–30.
VS-Games-2017-CobelloBMZ #aspect-oriented #community #education #experience #gamification #social: The value of establishing a community of teachers for the gamification of prosocial learning: Pegadogical, social and developmental aspects of a teachers' community space experience (SC, PPB, EM, NZ), pp. 189–192.
VS-Games-2017-MullerPGLJ #case study: Learning mechanical engineering in a virtual workshop: A preliminary study on utilisability, utility and acceptability (NM, DP, MG, PL, JPJ), pp. 55–62.
VS-Games-2017-PanzoliCPDOBLBG #biology #game studies: Learning the cell cycle with a game: Virtual experiments in cell biology (DP, SCB, JP, JD, MO, LB, VL, EB, FG, CPL, BD, YD), pp. 47–54.
VS-Games-2017-TsatsouVD #adaptation #case study #experience #modelling #multi: Modelling learning experiences in adaptive multi-agent learning environments (DT, NV, PD), pp. 193–200.
CIKM-2017-0001KGR #constraints #named: TaCLe: Learning Constraints in Tabular Data (SP0, SK, TG, LDR), pp. 2511–2514.
CIKM-2017-0002L #representation: Region Representation Learning via Mobility Flow (HW0, ZL), pp. 237–246.
CIKM-2017-Abu-El-HaijaPA #rank #symmetry: Learning Edge Representations via Low-Rank Asymmetric Projections (SAEH, BP, RAR), pp. 1787–1796.
CIKM-2017-BiegaGFGW #community #online: Learning to Un-Rank: Quantifying Search Exposure for Users in Online Communities (AJB, AG, HF, KPG, GW), pp. 267–276.
CIKM-2017-BouadjenekVZ #biology #sequence #using: Learning Biological Sequence Types Using the Literature (MRB, KV, JZ), pp. 1991–1994.
CIKM-2017-CavallariZCCC #community #detection #graph: Learning Community Embedding with Community Detection and Node Embedding on Graphs (SC, VWZ, HC, KCCC, EC), pp. 377–386.
CIKM-2017-ChaiLTS #multi: Compact Multiple-Instance Learning (JC, WL0, IWT, XBS), pp. 2007–2010.
CIKM-2017-ChenDWXCCM #detection #spreadsheet: Spreadsheet Property Detection With Rule-assisted Active Learning (ZC, SD, RW, GX, DC, MJC, JDM), pp. 999–1008.
CIKM-2017-DangCWZC #classification #kernel: Unsupervised Matrix-valued Kernel Learning For One Class Classification (SD, XC, YW0, JZ, FC0), pp. 2031–2034.
CIKM-2017-DehghaniRAF #query: Learning to Attend, Copy, and Generate for Session-Based Query Suggestion (MD0, SR, EA, PF), pp. 1747–1756.
CIKM-2017-EnsanBZK #empirical #rank: An Empirical Study of Embedding Features in Learning to Rank (FE, EB, AZ, AK), pp. 2059–2062.
CIKM-2017-FanGLXPC #visual notation #web: Learning Visual Features from Snapshots for Web Search (YF, JG, YL, JX0, LP, XC), pp. 247–256.
CIKM-2017-FuLL #named #network #representation: HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning (TYF, WCL, ZL), pp. 1797–1806.
CIKM-2017-HuangPLLMC #predict: An Ad CTR Prediction Method Based on Feature Learning of Deep and Shallow Layers (ZH, ZP, QL0, BL, HM, EC), pp. 2119–2122.
CIKM-2017-LeiLLZ #personalisation #ranking: Alternating Pointwise-Pairwise Learning for Personalized Item Ranking (YL, WL0, ZL, MZ), pp. 2155–2158.
CIKM-2017-LiCY #graph #recommendation: Learning Graph-based Embedding For Time-Aware Product Recommendation (YL, WC, HY), pp. 2163–2166.
CIKM-2017-LiDHTCL #network: Attributed Network Embedding for Learning in a Dynamic Environment (JL, HD, XH, JT, YC, HL0), pp. 387–396.
CIKM-2017-LiTZYW #recommendation #representation: Content Recommendation by Noise Contrastive Transfer Learning of Feature Representation (YL, GT, WZ0, YY0, JW0), pp. 1657–1665.
CIKM-2017-Liu0MLLM: A Two-step Information Accumulation Strategy for Learning from Highly Imbalanced Data (BL, MZ0, WM, XL0, YL, SM), pp. 1289–1298.
CIKM-2017-LyuHLP #collaboration #privacy #process #recognition: Privacy-Preserving Collaborative Deep Learning with Application to Human Activity Recognition (LL, XH, YWL, MP), pp. 1219–1228.
CIKM-2017-MansouriZRO0 #ambiguity #query #web: Learning Temporal Ambiguity in Web Search Queries (BM, MSZ, MR, FO, RC0), pp. 2191–2194.
CIKM-2017-MehrotraY #query #using: Task Embeddings: Learning Query Embeddings using Task Context (RM, EY), pp. 2199–2202.
CIKM-2017-Moon0S #graph: Learning Entity Type Embeddings for Knowledge Graph Completion (CM, PJ0, NFS), pp. 2215–2218.
CIKM-2017-Ni0ZYM #fine-grained #metric #similarity #using: Fine-grained Patient Similarity Measuring using Deep Metric Learning (JN, JL0, CZ, DY, ZM), pp. 1189–1198.
CIKM-2017-OosterhuisR17a #information retrieval #online #quality #rank: Balancing Speed and Quality in Online Learning to Rank for Information Retrieval (HO, MdR), pp. 277–286.
CIKM-2017-PangXCZ #category theory #detection: Selective Value Coupling Learning for Detecting Outliers in High-Dimensional Categorical Data (GP, HX, LC, WZ), pp. 807–816.
CIKM-2017-QianPS #scalability: Active Learning for Large-Scale Entity Resolution (KQ0, LP0, PS), pp. 1379–1388.
CIKM-2017-QuTSR00 #collaboration #framework #multi #network #representation: An Attention-based Collaboration Framework for Multi-View Network Representation Learning (MQ, JT0, JS, XR, MZ0, JH0), pp. 1767–1776.
CIKM-2017-SahaJHH #modelling #representation: Regularized and Retrofitted models for Learning Sentence Representation with Context (TKS, SRJ, NH, MAH), pp. 547–556.
CIKM-2017-ShiPW #modelling #student: Modeling Student Learning Styles in MOOCs (YS, ZP, HW), pp. 979–988.
CIKM-2017-TanZW #graph #representation #scalability: Representation Learning of Large-Scale Knowledge Graphs via Entity Feature Combinations (ZT, XZ0, WW0), pp. 1777–1786.
CIKM-2017-TengLW #detection #multi #network #using: Anomaly Detection in Dynamic Networks using Multi-view Time-Series Hypersphere Learning (XT, YRL, XW), pp. 827–836.
CIKM-2017-XiangJ #multimodal #network: Common-Specific Multimodal Learning for Deep Belief Network (CX, XJ), pp. 2387–2390.
CIKM-2017-XiaoMZLM #personalisation #recommendation #social: Learning and Transferring Social and Item Visibilities for Personalized Recommendation (XL0, MZ0, YZ, YL, SM), pp. 337–346.
CIKM-2017-XuLLX #rank: Learning to Rank with Query-level Semi-supervised Autoencoders (BX0, HL, YL0, KX), pp. 2395–2398.
CIKM-2017-Yang: When Deep Learning Meets Transfer Learning (QY), p. 5.
CIKM-2017-ZhangACC #recommendation #representation: Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources (YZ, QA, XC, WBC), pp. 1449–1458.
CIKM-2017-ZhangCYL #community #detection #enterprise #named: BL-ECD: Broad Learning based Enterprise Community Detection via Hierarchical Structure Fusion (JZ, LC, PSY, YL), pp. 859–868.
CIKM-2017-ZhangXKZ #graph #interactive: Learning Node Embeddings in Interaction Graphs (YZ, YX, XK, YZ), pp. 397–406.
CIKM-2017-ZhaoWLL: Missing Value Learning (ZLZ, CDW, KYL, JHL), pp. 2427–2430.
CIKM-2017-ZhaoXYYZFQ #image: Dual Learning for Cross-domain Image Captioning (WZ, WX, MY0, JY, ZZ, YF, YQ), pp. 29–38.
CIKM-2017-ZhouZL0: Learning Knowledge Embeddings by Combining Limit-based Scoring Loss (XZ, QZ, PL, LG0), pp. 1009–1018.
CIKM-2017-ZhuZHWZZY #collaboration #multi #recommendation: Broad Learning based Multi-Source Collaborative Recommendation (JZ, JZ, LH0, QW, BZ0, CZ, PSY), pp. 1409–1418.
CIKM-2017-ZohrevandGTSSS #framework: Deep Learning Based Forecasting of Critical Infrastructure Data (ZZ, UG, MAT, HYS, MS, AYS), pp. 1129–1138.
ECIR-2017-AlkhawaldehPJY #clustering #information retrieval #named #query: LTRo: Learning to Route Queries in Clustered P2P IR (RSA, DP0, JMJ, FY), pp. 513–519.
ECIR-2017-AyadiKHDJ #image #using: Learning to Re-rank Medical Images Using a Bayesian Network-Based Thesaurus (HA, MTK, JXH, MD, MBJ), pp. 160–172.
ECIR-2017-GuptaS: Learning to Classify Inappropriate Query-Completions (PG, JS), pp. 548–554.
ECIR-2017-RomeoMBM #approach #multi #ranking: A Multiple-Instance Learning Approach to Sentence Selection for Question Ranking (SR, GDSM, ABC, AM), pp. 437–449.
ECIR-2017-SoldainiG #approach #health #rank #semantics: Learning to Rank for Consumer Health Search: A Semantic Approach (LS, NG), pp. 640–646.
ICML-2017-0001N #composition #modelling #scalability: Relative Fisher Information and Natural Gradient for Learning Large Modular Models (KS0, FN), pp. 3289–3298.
ICML-2017-0004K: Follow the Moving Leader in Deep Learning (SZ0, JTK), pp. 4110–4119.
ICML-2017-0007MW #effectiveness: Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible (KZ0, WM, LW0), pp. 4130–4139.
ICML-2017-AgarwalS #difference #online #privacy: The Price of Differential Privacy for Online Learning (NA, KS), pp. 32–40.
ICML-2017-AlaaHS #process: Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis (AMA, SH, MvdS), pp. 60–69.
ICML-2017-AllamanisCKS #semantics: Learning Continuous Semantic Representations of Symbolic Expressions (MA, PC, PK, CAS), pp. 80–88.
ICML-2017-Allen-ZhuL17b #online #performance: Follow the Compressed Leader: Faster Online Learning of Eigenvectors and Faster MMWU (ZAZ, YL), pp. 116–125.
ICML-2017-AndreasKL #composition #multi #policy #sketching: Modular Multitask Reinforcement Learning with Policy Sketches (JA, DK, SL), pp. 166–175.
ICML-2017-AnschelBS #named #reduction: Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning (OA, NB, NS), pp. 176–185.
ICML-2017-AsadiL: An Alternative Softmax Operator for Reinforcement Learning (KA, MLL), pp. 243–252.
ICML-2017-AzarOM #bound: Minimax Regret Bounds for Reinforcement Learning (MGA, IO, RM), pp. 263–272.
ICML-2017-BachHRR #generative #modelling: Learning the Structure of Generative Models without Labeled Data (SHB, BDH, AR, CR), pp. 273–282.
ICML-2017-BachmanST #algorithm: Learning Algorithms for Active Learning (PB, AS, AT), pp. 301–310.
ICML-2017-BalleM #finite #policy: Spectral Learning from a Single Trajectory under Finite-State Policies (BB, OAM), pp. 361–370.
ICML-2017-BaramACM: End-to-End Differentiable Adversarial Imitation Learning (NB, OA, IC, SM), pp. 390–399.
ICML-2017-BarmannPS #online #optimisation: Emulating the Expert: Inverse Optimization through Online Learning (AB, SP, OS), pp. 400–410.
ICML-2017-BelangerYM #energy #network #predict: End-to-End Learning for Structured Prediction Energy Networks (DB, BY, AM), pp. 429–439.
ICML-2017-BelilovskyKVB #modelling #visual notation: Learning to Discover Sparse Graphical Models (EB, KK, GV, MBB), pp. 440–448.
ICML-2017-BellemareDM: A Distributional Perspective on Reinforcement Learning (MGB, WD, RM), pp. 449–458.
ICML-2017-BelloZVL: Neural Optimizer Search with Reinforcement Learning (IB, BZ, VV, QVL), pp. 459–468.
ICML-2017-BergmannJV: Learning Texture Manifolds with the Periodic Spatial GAN (UB, NJ, RV), pp. 469–477.
ICML-2017-BernsteinMSSHM #modelling #using #visual notation: Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models (GB, RM, TS, DS, MH, GM), pp. 478–487.
ICML-2017-BeygelzimerOZ #multi #online #performance: Efficient Online Bandit Multiclass Learning with Õ(√T) Regret (AB, FO, CZ), pp. 488–497.
ICML-2017-BojanowskiJ #predict: Unsupervised Learning by Predicting Noise (PB, AJ), pp. 517–526.
ICML-2017-BotevRB #optimisation: Practical Gauss-Newton Optimisation for Deep Learning (AB, HR, DB), pp. 557–565.
ICML-2017-ChebotarHZSSL #modelling: Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning (YC, KH, MZ, GSS, SS, SL), pp. 703–711.
ICML-2017-ChenHCDLBF: Learning to Learn without Gradient Descent by Gradient Descent (YC, MWH, SGC, MD, TPL, MB, NdF), pp. 748–756.
ICML-2017-ChenZLHH: Learning to Aggregate Ordinal Labels by Maximizing Separating Width (GC, SZ, DL, HH0, PAH), pp. 787–796.
ICML-2017-ChouMS #policy #probability #using: Improving Stochastic Policy Gradients in Continuous Control with Deep Reinforcement Learning using the Beta Distribution (PWC, DM, SAS), pp. 834–843.
ICML-2017-CortesGKMY #adaptation #named #network: AdaNet: Adaptive Structural Learning of Artificial Neural Networks (CC, XG, VK, MM, SY), pp. 874–883.
ICML-2017-DevlinUBSMK #named: RobustFill: Neural Program Learning under Noisy I/O (JD, JU, SB, RS, ArM, PK), pp. 990–998.
ICML-2017-FoersterNFATKW #experience #multi: Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning (JNF, NN, GF, TA, PHST, PK, SW), pp. 1146–1155.
ICML-2017-FutomaHH #classification #detection #multi #process: Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier (JF, SH, KAH), pp. 1174–1182.
ICML-2017-GalIG #image: Deep Bayesian Active Learning with Image Data (YG, RI, ZG), pp. 1183–1192.
ICML-2017-GaoFC #network: Local-to-Global Bayesian Network Structure Learning (TG, KPF, MC), pp. 1193–1202.
ICML-2017-GehringAGYD #sequence: Convolutional Sequence to Sequence Learning (JG, MA, DG, DY, YND), pp. 1243–1252.
ICML-2017-GravesBMMK #automation #education #network: Automated Curriculum Learning for Neural Networks (AG, MGB, JM, RM, KK), pp. 1311–1320.
ICML-2017-HaarnojaTAL #energy #policy: Reinforcement Learning with Deep Energy-Based Policies (TH, HT, PA, SL), pp. 1352–1361.
ICML-2017-HarandiSH #geometry #metric #reduction: Joint Dimensionality Reduction and Metric Learning: A Geometric Take (MTH, MS, RIH), pp. 1404–1413.
ICML-2017-HigginsPRMBPBBL #named: DARLA: Improving Zero-Shot Transfer in Reinforcement Learning (IH, AP, AAR, LM, CB, AP, MB, CB, AL), pp. 1480–1490.
ICML-2017-Hoffman #markov #modelling #monte carlo: Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo (MDH), pp. 1510–1519.
ICML-2017-HongHZ #algorithm #distributed #named #network #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-HuMTMS #self: Learning Discrete Representations via Information Maximizing Self-Augmented Training (WH, TM, ST, EM, MS), pp. 1558–1567.
ICML-2017-JabbariJKMR: Fairness in Reinforcement Learning (SJ, MJ, MJK, JM, AR0), pp. 1617–1626.
ICML-2017-JainMR #generative #modelling #multi #scalability: Scalable Generative Models for Multi-label Learning with Missing Labels (VJ, NM, PR), pp. 1636–1644.
ICML-2017-JerniteCS #classification #estimation: Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation (YJ, AC, DAS), pp. 1665–1674.
ICML-2017-KattOA #monte carlo: Learning in POMDPs with Monte Carlo Tree Search (SK, FAO, CA), pp. 1819–1827.
ICML-2017-KhasanovaF #graph #invariant #representation: Graph-based Isometry Invariant Representation Learning (RK, PF), pp. 1847–1856.
ICML-2017-KimCKLK #generative #network: Learning to Discover Cross-Domain Relations with Generative Adversarial Networks (TK, MC, HK, JKL, JK), pp. 1857–1865.
ICML-2017-KimPKH #named #network #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-KocaogluDV #graph: Cost-Optimal Learning of Causal Graphs (MK, AD, SV), pp. 1875–1884.
ICML-2017-KrishnamurthyAH #classification: Active Learning for Cost-Sensitive Classification (AK, AA, TKH, HDI, JL0), pp. 1915–1924.
ICML-2017-LawUZ #clustering: Deep Spectral Clustering Learning (MTL, RU, RSZ), pp. 1985–1994.
ICML-2017-LeeHPS #multi: Confident Multiple Choice Learning (KL, CH, KP, JS), pp. 2014–2023.
ICML-2017-LevyW #source code: Learning to Align the Source Code to the Compiled Object Code (DL, LW), pp. 2043–2051.
ICML-2017-LeY0L #coordination #multi: Coordinated Multi-Agent Imitation Learning (HML0, YY, PC0, PL), pp. 1995–2003.
ICML-2017-LivniCG #infinity #kernel #network: Learning Infinite Layer Networks Without the Kernel Trick (RL, DC, AG), pp. 2198–2207.
ICML-2017-LongZ0J #adaptation #network: Deep Transfer Learning with Joint Adaptation Networks (ML, HZ, JW0, MIJ), pp. 2208–2217.
ICML-2017-Luo #architecture #network: Learning Deep Architectures via Generalized Whitened Neural Networks (PL0), pp. 2238–2246.
ICML-2017-LvJL: Learning Gradient Descent: Better Generalization and Longer Horizons (KL, SJ, JL), pp. 2247–2255.
ICML-2017-MacGlashanHLPWR #feedback #interactive: Interactive Learning from Policy-Dependent Human Feedback (JM, MKH, RTL, BP, GW, DLR, MET, MLL), pp. 2285–2294.
ICML-2017-MachadoBB #framework: A Laplacian Framework for Option Discovery in Reinforcement Learning (MCM, MGB, MHB), pp. 2295–2304.
ICML-2017-MaystreG #approach #effectiveness #exclamation: Just Sort It! A Simple and Effective Approach to Active Preference Learning (LM, MG), pp. 2344–2353.
ICML-2017-MirhoseiniPLSLZ #optimisation: Device Placement Optimization with Reinforcement Learning (AM, HP, QVL, BS, RL0, YZ, NK, MN0, SB, JD), pp. 2430–2439.
ICML-2017-MohajerSE #rank: Active Learning for Top-K Rank Aggregation from Noisy Comparisons (SM, CS, AE), pp. 2488–2497.
ICML-2017-OhSLK #multi: Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning (JO, SPS, HL, PK), pp. 2661–2670.
ICML-2017-OmidshafieiPAHV #distributed #multi: Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability (SO, JP, CA, JPH, JV), pp. 2681–2690.
ICML-2017-OsbandR #question #why: Why is Posterior Sampling Better than Optimism for Reinforcement Learning? (IO, BVR), pp. 2701–2710.
ICML-2017-OsogamiKS #bidirectional #modelling: Bidirectional Learning for Time-series Models with Hidden Units (TO, HK, TS), pp. 2711–2720.
ICML-2017-PadSCTU #taxonomy: Dictionary Learning Based on Sparse Distribution Tomography (PP, FS, LEC, PT, MU), pp. 2731–2740.
ICML-2017-PentinaL #multi: Multi-task Learning with Labeled and Unlabeled Tasks (AP, CHL), pp. 2807–2816.
ICML-2017-PintoDSG #robust: Robust Adversarial Reinforcement Learning (LP, JD, RS, AG0), pp. 2817–2826.
ICML-2017-RiquelmeGL #estimation #linear #modelling: Active Learning for Accurate Estimation of Linear Models (CR, MG, AL), pp. 2931–2939.
ICML-2017-Shalev-ShwartzS: Failures of Gradient-Based Deep Learning (SSS, OS, SS), pp. 3067–3075.
ICML-2017-ShamirS #feedback #online #permutation: Online Learning with Local Permutations and Delayed Feedback (OS, LS), pp. 3086–3094.
ICML-2017-ShrikumarGK #difference: Learning Important Features Through Propagating Activation Differences (AS, PG, AK), pp. 3145–3153.
ICML-2017-SilverHHSGHDRRB #predict: The Predictron: End-To-End Learning and Planning (DS, HvH, MH, TS, AG, TH, GDA, DPR, NCR, AB, TD), pp. 3191–3199.
ICML-2017-SunRMW #named: meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting (XS0, XR, SM, HW), pp. 3299–3308.
ICML-2017-SunVGBB #predict: Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction (WS0, AV, GJG, BB, JAB), pp. 3309–3318.
ICML-2017-TandonLDK #distributed: Gradient Coding: Avoiding Stragglers in Distributed Learning (RT, QL, AGD, NK), pp. 3368–3376.
ICML-2017-TanM #modelling: Partitioned Tensor Factorizations for Learning Mixed Membership Models (ZT, SM0), pp. 3358–3367.
ICML-2017-ToshD: Diameter-Based Active Learning (CT, SD), pp. 3444–3452.
ICML-2017-UmlauftH #probability: Learning Stable Stochastic Nonlinear Dynamical Systems (JU, SH), pp. 3502–3510.
ICML-2017-UrschelBMR #process: Learning Determinantal Point Processes with Moments and Cycles (JU, VEB, AM, PR), pp. 3511–3520.
ICML-2017-VaswaniKWGLS #independence #online: Model-Independent Online Learning for Influence Maximization (SV, BK, ZW, MG, LVSL, MS), pp. 3530–3539.
ICML-2017-VezhnevetsOSHJS #network: FeUdal Networks for Hierarchical Reinforcement Learning (ASV, SO, TS, NH, MJ, DS, KK), pp. 3540–3549.
ICML-2017-VillegasYZSLL #predict: Learning to Generate Long-term Future via Hierarchical Prediction (RV, JY, YZ, SS, XL, HL), pp. 3560–3569.
ICML-2017-VorontsovTKP #dependence #network #on the #orthogonal: On orthogonality and learning recurrent networks with long term dependencies (EV, CT, SK, CP), pp. 3570–3578.
ICML-2017-WangKS0 #distributed #performance: Efficient Distributed Learning with Sparsity (JW, MK, NS, TZ0), pp. 3636–3645.
ICML-2017-WangLJK #kernel #optimisation: Batched High-dimensional Bayesian Optimization via Structural Kernel Learning (ZW, CL, SJ, PK), pp. 3656–3664.
ICML-2017-White #specification: Unifying Task Specification in Reinforcement Learning (MW), pp. 3742–3750.
ICML-2017-XiaQCBYL: Dual Supervised Learning (YX, TQ, WC0, JB0, NY, TYL), pp. 3789–3798.
ICML-2017-XieDZKYZX #constraints #modelling: Learning Latent Space Models with Angular Constraints (PX, YD, YZ, AK, YY, JZ, EPX), pp. 3799–3810.
ICML-2017-XuLZ #process #sequence: Learning Hawkes Processes from Short Doubly-Censored Event Sequences (HX, DL, HZ), pp. 3831–3840.
ICML-2017-YangFSH #clustering #towards: Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering (BY, XF0, NDS, MH), pp. 3861–3870.
ICML-2017-ZenkePG: Continual Learning Through Synaptic Intelligence (FZ, BP, SG), pp. 3987–3995.
ICML-2017-Zhang0KALZ #linear #modelling #named #precise: ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning (HZ, JL0, KK, DA, JL0, CZ), pp. 4035–4043.
ICML-2017-ZhangHTC: Re-revisiting Learning on Hypergraphs: Confidence Interval and Subgradient Method (CZ, SH, ZGT, THHC), pp. 4026–4034.
ICML-2017-ZhangZZHZ #distributed #network #online: Projection-free Distributed Online Learning in Networks (WZ0, PZ, WZ0, SCHH, TZ), pp. 4054–4062.
ICML-2017-ZhaoSE #generative #modelling: Learning Hierarchical Features from Deep Generative Models (SZ, JS, SE), pp. 4091–4099.
ICML-2017-ZhaoYKJB #architecture: Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture (MZ, SY, DK, TSJ, MTB), pp. 4100–4109.
ICML-2017-ZoghiTGKSW #modelling #online #probability #rank: Online Learning to Rank in Stochastic Click Models (MZ, TT, MG, BK, CS, ZW), pp. 4199–4208.
KDD-2017-0013H #paradigm #predict: Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics (XL0, JH), pp. 285–294.
KDD-2017-AmandH #composition #metric: Sparse Compositional Local Metric Learning (JSA, JH), pp. 1097–1104.
KDD-2017-AngelinoLASR: Learning Certifiably Optimal Rule Lists (EA, NLS, DA, MS, CR), pp. 35–44.
KDD-2017-ChoiBSSS #graph #named #representation: GRAM: Graph-based Attention Model for Healthcare Representation Learning (EC, MTB, LS, WFS, JS), pp. 787–795.
KDD-2017-DadkhahiM #detection #embedded #network: Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices (HD, BMM), pp. 1773–1781.
KDD-2017-DebGIPVYY #automation #named #network #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 #named #network #representation #scalability: metapath2vec: Scalable Representation Learning for Heterogeneous Networks (YD, NVC, AS), pp. 135–144.
KDD-2017-EmraniMX #multi #using: Prognosis and Diagnosis of Parkinson's Disease Using Multi-Task Learning (SE, AM, WX), pp. 1457–1466.
KDD-2017-How #nondeterminism #theory and practice: Planning and Learning under Uncertainty: Theory and Practice (JPH), p. 19.
KDD-2017-IosifidisN #scalability #sentiment: Large Scale Sentiment Learning with Limited Labels (VI, EN), pp. 1823–1832.
KDD-2017-LabutovHBH #mining: Semi-Supervised Techniques for Mining Learning Outcomes and Prerequisites (IL, YH0, PB, DH), pp. 907–915.
KDD-2017-LiuPH #distributed #multi: Distributed Multi-Task Relationship Learning (SL, SJP, QH), pp. 937–946.
KDD-2017-LuoZQYYWYW #functional #multi: Functional Annotation of Human Protein Coding Isoforms via Non-convex Multi-Instance Learning (TL, WZ, SQ, YY, DY, GW, JY, JW0), pp. 345–354.
KDD-2017-OvadiaHKLNPZS: Learning to Count Mosquitoes for the Sterile Insect Technique (YO, YH, DK, JL, DN, RP, TZ, DS), pp. 1943–1949.
KDD-2017-RibeiroSF #named: struc2vec: Learning Node Representations from Structural Identity (LFRR, PHPS, DRF), pp. 385–394.
KDD-2017-ShenHGC #comprehension #named: ReasoNet: Learning to Stop Reading in Machine Comprehension (YS, PSH, JG, WC), pp. 1047–1055.
KDD-2017-SpringS #random #scalability: Scalable and Sustainable Deep Learning via Randomized Hashing (RS, AS), pp. 445–454.
KDD-2017-TangW0M: End-to-end Learning for Short Text Expansion (JT, YW, KZ0, QM), pp. 1105–1113.
KDD-2017-TongKIYKSV #multi: Learning to Generate Rock Descriptions from Multivariate Well Logs with Hierarchical Attention (BT, MK, MI, TY, YK, AS, RV), pp. 2031–2040.
KDD-2017-UesakaMSKMAY #multi #visual notation: Multi-view Learning over Retinal Thickness and Visual Sensitivity on Glaucomatous Eyes (TU, KM, HS, TK, HM, RA, KY), pp. 2041–2050.
KDD-2017-WangYRTZYW #editing #recommendation: Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration (XW, LY, KR, GT, WZ0, YY0, JW0), pp. 2051–2059.
KDD-2017-XiaoGVT #behaviour: Learning Temporal State of Diabetes Patients via Combining Behavioral and Demographic Data (HX, JG0, LHV, DST), pp. 2081–2089.
KDD-2017-XieBLZ #distributed #multi #privacy: Privacy-Preserving Distributed Multi-Task Learning with Asynchronous Updates (LX, IMB, KL, JZ), pp. 1195–1204.
KDD-2017-YangBZY0 #approach #collaboration #recommendation: Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation (CY, LB, CZ0, QY0, JH0), pp. 1245–1254.
KDD-2017-YeZMPB #network: Learning from Labeled and Unlabeled Vertices in Networks (WY0, LZ, DM, CP, CB), pp. 1265–1274.
KDD-2017-YouX0T #education #multi #network: Learning from Multiple Teacher Networks (SY, CX0, CX0, DT), pp. 1285–1294.
KDD-2017-ZhangCTSS #effectiveness #multi #named: LEAP: Learning to Prescribe Effective and Safe Treatment Combinations for Multimorbidity (YZ, RC, JT0, WFS, JS), pp. 1315–1324.
KDD-2017-ZhanZ #induction #multi: Inductive Semi-supervised Multi-Label Learning with Co-Training (WZ, MLZ), pp. 1305–1314.
KDD-2017-ZhengBLL #detection #metric: Contextual Spatial Outlier Detection with Metric Learning (GZ, SLB, TL, ZL), pp. 2161–2170.
KDD-2017-ZhouLZCLYCYCDQ #distributed #named #parametricity: KunPeng: Parameter Server based Distributed Learning Systems and Its Applications in Alibaba and Ant Financial (JZ, XL, PZ, CC, LL, XY, QC, JY, XC, YD, Y(Q), pp. 1693–1702.
OOPSLA-2017-ChaeOHY #automation #generative #heuristic #program analysis: Automatically generating features for learning program analysis heuristics for C-like languages (KC, HO, KH, HY), p. 25.
OOPSLA-2017-SantolucitoZDSP #specification: Synthesizing configuration file specifications with association rule learning (MS, EZ, RD, AS, RP), p. 20.
OOPSLA-2017-SeidelSCWJ #data-driven #fault: Learning to blame: localizing novice type errors with data-driven diagnosis (ELS, HS, KC, WW, RJ), p. 27.
OOPSLA-2017-WuCC #error message: Learning user friendly type-error messages (BW, JPCI, SC0), p. 29.
PADL-2017-Vennekens #api #declarative #programming #python: Lowering the Learning Curve for Declarative Programming: A Python API for the IDP System (JV), pp. 86–102.
POPL-2017-MoermanS0KS #automaton: Learning nominal automata (JM, MS, AS0, BK, MS), pp. 613–625.
PPDP-2017-HoweRK #symmetry: Theory learning with symmetry breaking (JMH, ER, AK), pp. 85–96.
SAS-2017-BrockschmidtCKK #analysis: Learning Shape Analysis (MB, YC, PK, SK, DT), pp. 66–87.
ASE-2017-JamshidiSVKPA #analysis #configuration management #modelling #performance: Transfer learning for performance modeling of configurable systems: an exploratory analysis (PJ, NS, MV, CK, AP, YA), pp. 497–508.
ASE-2017-Krishna #effectiveness: Learning effective changes for software projects (RK), pp. 1002–1005.
ASE-2017-RafiqDRBYSLCPN #adaptation #network #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-FuM #case study: Easy over hard: a case study on deep learning (WF0, TM), pp. 49–60.
ESEC-FSE-2017-FuM17a #fault #predict: Revisiting unsupervised learning for defect prediction (WF0, TM), pp. 72–83.
ESEC-FSE-2017-LeeHLKJ #automation #debugging #industrial: Applying deep learning based automatic bug triager to industrial projects (SRL, MJH, CGL, MK, GJ), pp. 926–931.
ESEC-FSE-2017-MuraliCJ #api #fault #specification: Bayesian specification learning for finding API usage errors (VM, SC, CJ), pp. 151–162.
ICSE-2017-0004CC #semantics #traceability #using: Semantically enhanced software traceability using deep learning techniques (JG0, JC, JCH), pp. 3–14.
ICSE-2017-ChenBHXZX #compilation #source code #testing: Learning to prioritize test programs for compiler testing (JC0, YB, DH, YX, HZ0, BX), pp. 700–711.
ICSE-2017-RolimSDPGGSH #program transformation: Learning syntactic program transformations from examples (RR, GS, LD, OP, SG, RG, RS, BH), pp. 404–415.
GPCE-2017-MartiniH #automation #case study #experience #generative: Automatic generation of virtual learning spaces driven by CaVaDSL: an experience report (RGM, PRH), pp. 233–245.
ASPLOS-2017-LiCCZ #modelling #named #topic: SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs (KL, JC0, WC, JZ0), pp. 497–509.
CASE-2017-ChuckLKJFG #automation #statistics: Statistical data cleaning for deep learning of automation tasks from demonstrations (CC, ML, SK, RJ, RF, KG), pp. 1142–1149.
CASE-2017-GohSS #modelling #predict: A model-based learning controller with predictor augmentation for non-stationary conditions and time delay in water shooting (CFG, GLGS, KS), pp. 1110–1117.
CASE-2017-HanPM #approach #linear #modelling: Model-based reinforcement learning approach for deformable linear object manipulation (HH, GP, TM), pp. 750–755.
CASE-2017-KapadiaSJG #named: EchoBot: Facilitating data collection for robot learning with the Amazon echo (RK, SS, LJ, KG), pp. 159–165.
CASE-2017-LaiJG #energy #parametricity #predict: An integrated physical-based and parameter learning method for ship energy prediction under varying operating conditions (XL, XJ, XG), pp. 1180–1185.
CASE-2017-LiangMLLG #automation #industrial #using: Using dVRK teleoperation to facilitate deep learning of automation tasks for an industrial robot (JL, JM, ML, PL, KG), pp. 1–8.
CASE-2017-LiXLK #approach #classification #physics: Improving colorectal polyp classification based on physical examination data - A ensemble learning approach (CL, XX, JL, NK), pp. 193–194.
CASE-2017-LiXZ #analysis #complexity: Complexity analysis of reinforcement learning and its application to robotics (BL, LX, QZ), pp. 1425–1426.
CASE-2017-LuRSW #detection #visual notation: Visual guided deep learning scheme for fall detection (NL, XR, JS, YW), pp. 801–806.
CASE-2017-PengZH #distributed #fault: Distributed fault diagnosis with shared-basis and B-splines-based matched learning (CP, YZ, QH), pp. 536–541.
CASE-2017-RenWJ #equivalence: Engineering effect equivalence enabled transfer learning (JR, HW, XJ), pp. 1174–1179.
CASE-2017-SunLZJ #framework #functional #using: Exploring functional variant using a deep learning framework (TS, ZL, XMZ, RJ), pp. 98–99.
CASE-2017-ZhaoCDW #fault #multi #taxonomy: TQWT-based multi-scale dictionary learning for rotating machinery fault diagnosis (ZZ, XC, BD, SW), pp. 554–559.
CASE-2017-ZhengLC #comparison #policy #realtime: Comparison study of two reinforcement learning based real-time control policies for two-machine-one-buffer production system (WZ, YL, QC), pp. 1163–1168.
CASE-2017-ZhouWY #approach: Dynamic dispatching for re-entrant production lines - A deep learning approach (FYZ, CHW, CJY), pp. 1026–1031.
CGO-2017-OgilvieP0L #compilation #cost analysis: Minimizing the cost of iterative compilation with active learning (WFO, PP, ZW0, HL), pp. 245–256.
CAV-2017-BielikRV: Learning a Static Analyzer from Data (PB, VR, MTV), pp. 233–253.
CAV-2017-Vazquez-Chanlatte #clustering #logic: Logical Clustering and Learning for Time-Series Data (MVC, JVD, XJ, SAS), pp. 305–325.
CSL-2017-AngluinAF #polynomial #query: Query Learning of Derived Omega-Tree Languages in Polynomial Time (DA, TA, DF), p. 21.
CSL-2017-HeerdtS0 #automaton #category theory #framework #named: CALF: Categorical Automata Learning Framework (GvH, MS, AS0), p. 24.
ICST-2017-TapplerAB #automaton #communication #modelling #testing: Model-Based Testing IoT Communication via Active Automata Learning (MT, BKA, RB), pp. 276–287.
ICTSS-2017-MaAYE #execution #testing: Fragility-Oriented Testing with Model Execution and Reinforcement Learning (TM, SA0, TY0, ME), pp. 3–20.
CSEET-2016-DaunSWPT #case study #experience #industrial #requirements: Project-Based Learning with Examples from Industry in University Courses: An Experience Report from an Undergraduate Requirements Engineering Course (MD, AS, TW, KP, BT), pp. 184–193.
CSEET-2016-FreitasSM #student #using: Using an Active Learning Environment to Increase Students' Engagement (SAAdF, WCMPS, GM), pp. 232–236.
CSEET-2016-GeorgasPM #architecture #runtime #using #visualisation: Supporting Software Architecture Learning Using Runtime Visualization (JCG, JDP, MJM), pp. 101–110.
CSEET-2016-LetouzeSS #case study #generative #web: Generating Software Engineers by Developing Web Systems: A Project-Based Learning Case Study (PL, JIMdS, VMDS), pp. 194–203.
CSEET-2016-ShutoWKFYO #education #effectiveness #re-engineering: Learning Effectiveness of Team Discussions in Various Software Engineering Education Courses (MS, HW, KK, YF, SY, MO), pp. 227–231.
CSEET-2016-SunagaSWKFYO #effectiveness #question: Which Combinations of Personal Characteristic Types are more Effective in Different Project-Based Learning Courses? (YS, MS, HW, KK, YF, SY, MO), pp. 137–141.
EDM-2016-BhartiyaCBSM #documentation #segmentation: Document Segmentation for Labeling with Academic Learning Objectives (DB, DC, SB, BS, MKM), pp. 282–287.
EDM-2016-BuffumFBWML #assessment #collaboration #embedded #mining #sequence: Mining Sequences of Gameplay for Embedded Assessment in Collaborative Learning (PSB, MF, KEB, ENW, BWM, JCL), pp. 575–576.
EDM-2016-ChoiLHLRW #data-driven #interactive: Exploring Learning Management System Interaction Data: Combining Data-driven and Theory-driven Approaches (HC, JEL, WJH, KL, MR, AW), pp. 324–329.
EDM-2016-CraigHXFH #behaviour #identification #persistent #predict: Identifying relevant user behavior and predicting learning and persistence in an ITS-based afterschool program (SDC, XH, JX, YF, XH), pp. 581–582.
EDM-2016-CutumisuS #assessment #feedback #game studies: Choosing versus Receiving Feedback: The Impact of Feedback Valence on Learning in an Assessment Game (MC, DLS), pp. 341–346.
EDM-2016-DaiAY #analysis #recommendation #towards: Course Content Analysis: An Initiative Step toward Learning Object Recommendation Systems for MOOC Learners (YD, YA, MY), pp. 347–352.
EDM-2016-DavisCHH: Gauging MOOC Learners' Adherence to the Designed Learning Path (DD, GC, CH, GJH), pp. 54–61.
EDM-2016-DianaESK #metric #self #student: Extracting Measures of Active Learning and Student Self-Regulated Learning Strategies from MOOC Data (ND, ME, JCS, KRK), pp. 583–584.
EDM-2016-DibieSMQ #community #online #social: Exploring Social Influence on the Usage of Resources in an Online Learning Community (OD, TS, KEM, DQ), pp. 585–586.
EDM-2016-DominguezBU #modelling #predict: Predicting STEM Achievement with Learning Management System Data: Prediction Modeling and a Test of an Early Warning System (MD, MLB, PMU), pp. 589–590.
EDM-2016-DongKB #comparison #mining #multi #process: Comparison of Selection Criteria for Multi-Feature Hierarchical Activity Mining in Open Ended Learning Environments (YD, JSK, GB), pp. 591–592.
EDM-2016-FeildLZRE #automation #feedback #framework #platform #scalability: A Scalable Learning Analytics Platform for Automated Writing Feedback (JLF, NL, NLZ, MR, AE), pp. 688–693.
EDM-2016-HuangB #framework #modelling #student #towards: Towards Modeling Chunks in a Knowledge Tracing Framework for Students' Deep Learning (YH0, PB), pp. 666–668.
EDM-2016-HuttMWDD #detection: The Eyes Have It: Gaze-based Detection of Mind Wandering during Learning with an Intelligent Tutoring System (SH, CM, SW, PJD, SKD), pp. 86–93.
EDM-2016-JoL #how #online: How to Judge Learning on Online Learning: Minimum Learning Judgment System (JJ, HL), pp. 597–598.
EDM-2016-JoTFRG #behaviour #modelling #social: Expediting Support for Social Learning with Behavior Modeling (YJ, GT, OF, CPR, DG), pp. 400–405.
EDM-2016-Kay #people: Enabling people to harness and control EDM for lifelong, life-wide learning (JK), p. 4.
EDM-2016-Kay16a #people: Enabling people to harness and control EDM for lifelong, life-wide learning (JK), pp. 10–20.
EDM-2016-LabutovL #web: Web as a textbook: Curating Targeted Learning Paths through the Heterogeneous Learning Resources on the Web (IL, HL), pp. 110–118.
EDM-2016-LanB #framework #personalisation: A Contextual Bandits Framework for Personalized Learning Action Selection (ASL, RGB), pp. 424–429.
EDM-2016-LeeRBY #analysis #approach #clustering #heatmap #interactive #visualisation: Hierarchical Cluster Analysis Heatmaps and Pattern Analysis: An Approach for Visualizing Learning Management System Interaction Data (JEL, MR, AB, MY), pp. 603–604.
EDM-2016-Linn: WISE Ways to Strengthen Inquiry Science Learning (MCL), p. 3.
EDM-2016-MacLellanHPK #architecture #education: The Apprentice Learner architecture: Closing the loop between learning theory and educational data (CJM, EH, RP, KRK), pp. 151–158.
EDM-2016-Nam #adaptation #behaviour #predict: Predicting Off-task Behaviors for Adaptive Vocabulary Learning System (SN), pp. 672–674.
EDM-2016-NgHLK #modelling #sequence #using: Modelling the way: Using action sequence archetypes to differentiate learning pathways from learning outcomes (KHRN, KH, KL, AWHK), pp. 167–174.
EDM-2016-NiuNZWKY #algorithm #clustering: A Coupled User Clustering Algorithm for Web-based Learning Systems (KN, ZN, XZ, CW, KK, MY), pp. 175–182.
EDM-2016-QuigleyDSHPSAP: Equity of Learning Opportunities in the Chicago City of Learning Program (DQ, OD, MAS, KVH, WRP, TS, UA, NP), pp. 618–619.
EDM-2016-Rau #concept #mining #physics #social: Pattern mining uncovers social prompts of conceptual learning with physical and virtual representations (MAR), pp. 478–483.
EDM-2016-RauMN16a #how #similarity #visual notation: How to Model Implicit Knowledge? Similarity Learning Methods to Assess Perceptions of Visual Representations (MAR, BM, RDN), pp. 199–206.
EDM-2016-RauMN16a_ #how #similarity #visual notation: How to Model Implicit Knowledge? Similarity Learning Methods to Assess Perceptions of Visual Representations (MAR, BM, RDN), pp. 199–206.
EDM-2016-RoweAEHBBE #game studies #metric #validation: Validating Game-based Measures of Implicit Science Learning (ER, JAC, ME, AH, TB, RB, TE), pp. 490–495.
EDM-2016-Sande #component #multi #problem: Learning Curves for Problems with Multiple Knowledge Components (BvdS), pp. 523–526.
EDM-2016-Sande16a #analysis #component #problem: Learning curves versus problem difficulty: an analysis of the Knowledge Component picture for a given context (BvdS), pp. 646–647.
EDM-2016-ShenC #feature model #modelling: Aim Low: Correlation-based Feature Selection for Model-based Reinforcement Learning (SS, MC), pp. 507–512.
EDM-2016-SlaterOBSIH #problem #semantics #student: Semantic Features of Math Problems: Relationships to Student Learning and Engagement (SS, JO, RSB, PS, PSI, NTH), pp. 223–230.
EDM-2016-SnowKPFB #how #online #performance #student: Quantifying How Students Use an Online Learning System: A Focus on Transitions and Performance (ELS, AEK, TEP, MF, AJB), pp. 640–641.
EDM-2016-StapelZP #online #performance #predict #student: An Ensemble Method to Predict Student Performance in an Online Math Learning Environment (MS, ZZ, NP), pp. 231–238.
EDM-2016-SunY #community #online #personalisation: Personalization of Learning Paths in Online Communities of Creators (MS, SY), pp. 513–516.
EDM-2016-Wang #concept #design #interactive #personalisation: Designing Interactive and Personalized Concept Mapping Learning Environments (SW0), pp. 678–680.
EDM-2016-WenMWDHR #collaboration #integration #online #predict: Transactivity as a Predictor of Future Collaborative Knowledge Integration in Team-Based Learning in Online Courses (MW, KM, XW0, SD, JDH, CPR), pp. 533–538.
EDM-2016-YadavSKSD #framework #named #platform: TutorSpace: Content-centric Platform for Enabling Blended Learning in Developing Countries (KY, KS, RK, SS, OD), pp. 705–706.
EDM-2016-Yee-Kingd #collaboration #markov #online #process #social: Stimulating collaborative activity in online social learning environments with Markov decision processes (MYK, Md), pp. 652–653.
EDM-2016-Yee-KingGd #collaboration #metric #online #predict #social #student #using: Predicting student grades from online, collaborative social learning metrics using K-NN (MYK, AGR, Md), pp. 654–655.
EDM-2016-ZhangSC #automation #clustering #effectiveness #modelling #student: Deep Learning + Student Modeling + Clustering: a Recipe for Effective Automatic Short Answer Grading (YZ, RS, MC), pp. 562–567.
EDM-2016-ZhengSP #exclamation #student: Perfect Scores Indicate Good Students !? The Case of One Hundred Percenters in a Math Learning System (ZZ, MS, NP), pp. 660–661.
ICPC-2016-TianWLG #debugging #rank #recommendation: Learning to rank for bug report assignee recommendation (YT0, DW, DL0, CLG), pp. 1–10.
ICSME-2016-YeXFLK #api #natural language: Learning to Extract API Mentions from Informal Natural Language Discussions (DY, ZX, CYF, JL0, NK), pp. 389–399.
FM-2016-ChenP0 #cyber-physical #invariant #towards #verification: Towards Learning and Verifying Invariants of Cyber-Physical Systems by Code Mutation (YC0, CMP, JS0), pp. 155–163.
FM-2016-GiantamidisT: Learning Moore Machines from Input-Output Traces (GG, ST), pp. 291–309.
IFM-2016-BosSV #automaton #metric: Enhancing Automata Learning by Log-Based Metrics (PvdB, RS, FWV), pp. 295–310.
IFM-2016-SchutsHV #case study #equivalence #experience #industrial #legacy #refactoring #using: Refactoring of Legacy Software Using Model Learning and Equivalence Checking: An Industrial Experience Report (MS, JH, FWV), pp. 311–325.
ICFP-2016-Abadi #named #scalability: TensorFlow: learning functions at scale (MA), p. 1.
AIIDE-2016-HarrisonR #crowdsourcing #using: Learning From Stories: Using Crowdsourced Narratives to Train Virtual Agents (BH, MOR), pp. 183–189.
AIIDE-2016-SummervilleM #design: Mystical Tutor: A Magic: The Gathering Design Assistant via Denoising Sequence-to-Sequence Learning (AJS, MM), pp. 86–92.
CHI-PLAY-2016-BonsignoreHKVF #game studies #people: Roles People Play: Key Roles Designed to Promote Participation and Learning in Alternate Reality Games (EB, DLH, KK, AV, AF), pp. 78–90.
CIG-2016-Bursztein #statistics #using: I am a legend: Hacking hearthstone using statistical learning methods (EB), pp. 1–8.
CIG-2016-CazenaveLTT #game studies #random #using: Learning opening books in partially observable games: Using random seeds in Phantom Go (TC, JL0, FT, OT), pp. 1–7.
CIG-2016-ChuIHT #game studies #video: Position-based reinforcement learning biased MCTS for General Video Game Playing (CYC, SI, TH, RT), pp. 1–8.
CIG-2016-KempkaWRTJ #framework #named #platform #research #visual notation: ViZDoom: A Doom-based AI research platform for visual reinforcement learning (MK, MW, GR, JT, WJ), pp. 1–8.
CIG-2016-Shaker #framework #generative #motivation: Intrinsically motivated reinforcement learning: A promising framework for procedural content generation (NS), pp. 1–8.
CIG-2016-ShakerA #experience #predict: Transfer learning for cross-game prediction of player experience (NS, MAZ), pp. 1–8.
CIG-2016-ShiC #generative #online: Online level generation in Super Mario Bros via learning constructive primitives (PS, KC0), pp. 1–8.
CIG-2016-SifaSDOB #game studies #predict #representation: Predicting Retention in Sandbox Games with Tensor Factorization-based Representation Learning (RS, SS, AD, CO, CB), pp. 1–8.
CIG-2016-SungurS #algorithm #behaviour: Voluntary behavior on cortical learning algorithm based agents (AKS, ES), pp. 1–7.
DiGRA-FDG-2016-MelcerI #design #framework #game studies #physics #simulation: Bridging the Physical Learning Divides: A Design Framework for Embodied Learning Games and Simulations (EFM, KI).
VS-Games-2016-FerreiraGH #case study #design #education #game studies: Game Based Learning: A Case Study on Designing an Educational Game for Children in Developing Countries (SMF, CGV, RH), pp. 1–8.
VS-Games-2016-GauthierJ #game studies #problem #research: Woes of an RCT for Game-Based Learning Research - Past Problems and Potential Solutions (AG, JJ), pp. 1–2.
VS-Games-2016-GomezC #design #development #game studies #prototype: Bridging Design Prototypes in the Development of Games for Formal Learning Environments (GG, DC), pp. 1–5.
VS-Games-2016-PatinoRP #analysis #game studies: Analysis of Game and Learning Mechanics According to the Learning Theories (AP, MR, JNP), pp. 1–4.
VS-Games-2016-RamosP #game studies: Program with Ixquic: Educative Games and Learning in Augmented and Virtual Environments (CR, TP), pp. 1–2.
CIKM-2016-0001H #adaptation #interactive #multi #named #using: aptMTVL: Nailing Interactions in Multi-Task Multi-View Multi-Label Learning using Adaptive-basis Multilinear Factor Analyzers (XL0, JH), pp. 1171–1180.
CIKM-2016-0064NRR #detection #framework #identification #multi: A Multiple Instance Learning Framework for Identifying Key Sentences and Detecting Events (WW0, YN, HR, NR), pp. 509–518.
CIKM-2016-AmandH: Discriminative View Learning for Single View Co-Training (JSA, JH), pp. 2221–2226.
CIKM-2016-BaruahZGLSV #optimisation: Optimizing Nugget Annotations with Active Learning (GB, HZ0, RG, JJL, MDS, OV), pp. 2359–2364.
CIKM-2016-ChenOX #recommendation: Learning Points and Routes to Recommend Trajectories (DC, CSO, LX), pp. 2227–2232.
CIKM-2016-CheungL #rank #robust #scalability: Scalable Spectral k-Support Norm Regularization for Robust Low Rank Subspace Learning (YmC, JL), pp. 1151–1160.
CIKM-2016-CormackG #classification #reliability #scalability: Scalability of Continuous Active Learning for Reliable High-Recall Text Classification (GVC, MRG), pp. 1039–1048.
CIKM-2016-DeveaudMN #rank: Learning to Rank System Configurations (RD, JM, JYN), pp. 2001–2004.
CIKM-2016-FengXZ #distributed: Distributed Deep Learning for Question Answering (MF, BX, BZ), pp. 2413–2416.
CIKM-2016-GyselRK: Learning Latent Vector Spaces for Product Search (CVG, MdR, EK), pp. 165–174.
CIKM-2016-HanSBW: Routing an Autonomous Taxi with Reinforcement Learning (MH, PS, SB, HW0), pp. 2421–2424.
CIKM-2016-HeTOKYC #query: Learning to Rewrite Queries (YH, JT, HO, CK, DY, YC), pp. 1443–1452.
CIKM-2016-KhabsaCAZAW #metric: Learning to Account for Good Abandonment in Search Success Metrics (MK, ACC, AHA, IZ, TA, KW), pp. 1893–1896.
CIKM-2016-LiSNLF #hashtag #rank #recommendation #topic #twitter: Hashtag Recommendation Based on Topic Enhanced Embedding, Tweet Entity Data and Learning to Rank (QL, SS, AN, XL, RF), pp. 2085–2088.
CIKM-2016-MartinoBR0M #community #using: Learning to Re-Rank Questions in Community Question Answering Using Advanced Features (GDSM, ABC, SR, AU0, AM), pp. 1997–2000.
CIKM-2016-RenZRZYW #optimisation #performance: User Response Learning for Directly Optimizing Campaign Performance in Display Advertising (KR, WZ0, YR, HZ, YY0, JW0), pp. 679–688.
CIKM-2016-SilvaGAG #rank: Compression-Based Selective Sampling for Learning to Rank (RMS, GdCMG, MSA, MAG), pp. 247–256.
CIKM-2016-SousaCRMG #feature model #rank: Incorporating Risk-Sensitiveness into Feature Selection for Learning to Rank (DXdS, SDC, TCR, WSM, MAG), pp. 257–266.
CIKM-2016-TymoshenkoBM #rank #web: Learning to Rank Non-Factoid Answers: Comment Selection in Web Forums (KT, DB, AM), pp. 2049–2052.
CIKM-2016-WangJYJM #using: Learning to Extract Conditional Knowledge for Question Answering using Dialogue (PW, LJ, JY0, LJ, WYM), pp. 277–286.
CIKM-2016-WangWW: Learning Hidden Features for Contextual Bandits (HW, QW, HW), pp. 1633–1642.
CIKM-2016-XieWY: Active Zero-Shot Learning (SX, SW, PSY), pp. 1889–1892.
CIKM-2016-XieYWXCW #graph #recommendation: Learning Graph-based POI Embedding for Location-based Recommendation (MX, HY, HW, FX, WC, SW), pp. 15–24.
CIKM-2016-YuanGJCYZ #named #ranking #using: LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates (FY, GG, JMJ, LC0, HY, WZ0), pp. 227–236.
CIKM-2016-ZhaoK #online #rank #reliability: Constructing Reliable Gradient Exploration for Online Learning to Rank (TZ, IK), pp. 1643–1652.
CIKM-2016-ZhengC #classification #constraints #probability: Regularizing Structured Classifier with Conditional Probabilistic Constraints for Semi-supervised Learning (VWZ, KCCC), pp. 1029–1038.
CIKM-2016-ZhengW #graph #multi: Graph-Based Multi-Modality Learning for Clinical Decision Support (ZZ, XW0), pp. 1945–1948.
CIKM-2016-ZhuangLPXH #adaptation: Ensemble of Anchor Adapters for Transfer Learning (FZ, PL0, SJP, HX, QH), pp. 2335–2340.
ECIR-2016-AlmasriBC #comparison #feedback #pseudo #query: A Comparison of Deep Learning Based Query Expansion with Pseudo-Relevance Feedback and Mutual Information (MA, CB, JPC), pp. 709–715.
ECIR-2016-BotevaGSR #dataset #information retrieval #rank: A Full-Text Learning to Rank Dataset for Medical Information Retrieval (VB, DGG, AS, SR), pp. 716–722.
ECIR-2016-CroceB #kernel #scalability: Large-Scale Kernel-Based Language Learning Through the Ensemble Nystr đdoto o ¨ m Methods (DC, RB0), pp. 100–112.
ECIR-2016-IencoRRRT #mining #modelling #multi: MultiLingMine 2016: Modeling, Learning and Mining for Cross/Multilinguality (DI, MR, SR, PR, AT), pp. 869–873.
ECIR-2016-LiWPA #analysis #empirical #sentiment: An Empirical Study of Skip-Gram Features and Regularization for Learning on Sentiment Analysis (CL, BW, VP, JAA), pp. 72–87.
ECIR-2016-MiottoLD #health #predict: Deep Learning to Predict Patient Future Diseases from the Electronic Health Records (RM, LL0, JTD), pp. 768–774.
ECIR-2016-MustoSGL #recommendation #wiki #word: Learning Word Embeddings from Wikipedia for Content-Based Recommender Systems (CM, GS, MdG, PL), pp. 729–734.
ECIR-2016-NiuLC #approach #named #twitter: LExL: A Learning Approach for Local Expert Discovery on Twitter (WN, ZL, JC), pp. 803–809.
ECIR-2016-WangGLXC #multi #predict #representation: Multi-task Representation Learning for Demographic Prediction (PW, JG, YL, JX0, XC), pp. 88–99.
ECIR-2016-ZhangDW #case study #category theory #multi #predict: Deep Learning over Multi-field Categorical Data - - A Case Study on User Response Prediction (WZ0, TD, JW0), pp. 45–57.
ICML-2016-AJFMS #cumulative #predict: Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control (PLA, CJ, MCF0, SIM, CS), pp. 1406–1415.
ICML-2016-AkrourNAA #optimisation: Model-Free Trajectory Optimization for Reinforcement Learning (RA, GN, HA, AA), pp. 2961–2970.
ICML-2016-AroraMM #multi #optimisation #probability #representation #using: Stochastic Optimization for Multiview Representation Learning using Partial Least Squares (RA, PM, TVM), pp. 1786–1794.
ICML-2016-BaiRWS #classification #difference #geometry: Differential Geometric Regularization for Supervised Learning of Classifiers (QB, SR, ZW, SS), pp. 1879–1888.
ICML-2016-BalkanskiMKS #combinator: Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization (EB, BM, AK0, YS), pp. 2207–2216.
ICML-2016-CohenHK #feedback #graph #online: Online Learning with Feedback Graphs Without the Graphs (AC, TH, TK), pp. 811–819.
ICML-2016-DaneshmandLH #adaptation: Starting Small - Learning with Adaptive Sample Sizes (HD, AL, TH), pp. 1463–1471.
ICML-2016-DuanCHSA #benchmark #metric: Benchmarking Deep Reinforcement Learning for Continuous Control (YD, XC0, RH, JS, PA), pp. 1329–1338.
ICML-2016-FernandoG #classification #video: Learning End-to-end Video Classification with Rank-Pooling (BF, SG), pp. 1187–1196.
ICML-2016-FinnLA #optimisation #policy: Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization (CF, SL, PA), pp. 49–58.
ICML-2016-FriesenD #modelling #theorem: The Sum-Product Theorem: A Foundation for Learning Tractable Models (ALF, PMD), pp. 1909–1918.
ICML-2016-GalG #approximate #nondeterminism #representation: Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning (YG, ZG), pp. 1050–1059.
ICML-2016-GlaudeP #automaton #probability: PAC learning of Probabilistic Automaton based on the Method of Moments (HG, OP), pp. 820–829.
ICML-2016-GuanRW #markov #multi #performance #process #recognition #using: Efficient Multi-Instance Learning for Activity Recognition from Time Series Data Using an Auto-Regressive Hidden Markov Model (XG, RR, WKW), pp. 2330–2339.
ICML-2016-HammCB #multi: Learning privately from multiparty data (JH, YC, MB), pp. 555–563.
ICML-2016-HashimotoGJ #generative: Learning Population-Level Diffusions with Generative RNNs (TBH, DKG, TSJ), pp. 2417–2426.
ICML-2016-HeB #modelling: Opponent Modeling in Deep Reinforcement Learning (HH0, JLBG), pp. 1804–1813.
ICML-2016-HoGE #optimisation #policy: Model-Free Imitation Learning with Policy Optimization (JH, JKG, SE), pp. 2760–2769.
ICML-2016-JiangL #evaluation #robust: Doubly Robust Off-policy Value Evaluation for Reinforcement Learning (NJ, LL0), pp. 652–661.
ICML-2016-JohanssonSS: Learning Representations for Counterfactual Inference (FDJ, US, DAS), pp. 3020–3029.
ICML-2016-Kasiviswanathan #empirical #performance: Efficient Private Empirical Risk Minimization for High-dimensional Learning (SPK, HJ), pp. 488–497.
ICML-2016-KatariyaKSW #multi #rank: DCM Bandits: Learning to Rank with Multiple Clicks (SK, BK, CS, ZW), pp. 1215–1224.
ICML-2016-KawakitaT: Barron and Cover's Theory in Supervised Learning and its Application to Lasso (MK, JT), pp. 1958–1966.
ICML-2016-LeeYH #multi #symmetry: Asymmetric Multi-task Learning based on Task Relatedness and Confidence (GL, EY, SJH), pp. 230–238.
ICML-2016-LeKYC #online #predict #sequence: Smooth Imitation Learning for Online Sequence Prediction (HML0, AK, YY, PC0), pp. 680–688.
ICML-2016-LererGF #physics: Learning Physical Intuition of Block Towers by Example (AL, SG, RF), pp. 430–438.
ICML-2016-LiuSSF #markov #network: Structure Learning of Partitioned Markov Networks (SL0, TS, MS, KF), pp. 439–448.
ICML-2016-LiuY #multi: Cross-Graph Learning of Multi-Relational Associations (HL, YY), pp. 2235–2243.
ICML-2016-LiZALH #optimisation #probability: Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning (XL, TZ, RA, HL0, JDH), pp. 917–925.
ICML-2016-LiZZ #memory management: Learning to Generate with Memory (CL, JZ0, BZ0), pp. 1177–1186.
ICML-2016-LouizosW #matrix #performance: Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors (CL, MW), pp. 1708–1716.
ICML-2016-MenschMTV #matrix #taxonomy: Dictionary Learning for Massive Matrix Factorization (AM, JM, BT, GV), pp. 1737–1746.
ICML-2016-MnihBMGLHSK: Asynchronous Methods for Deep Reinforcement Learning (VM, APB, MM, AG, TPL, TH, DS, KK), pp. 1928–1937.
ICML-2016-MussmannE: Learning and Inference via Maximum Inner Product Search (SM, SE), pp. 2587–2596.
ICML-2016-NiepertAK #graph #network: Learning Convolutional Neural Networks for Graphs (MN, MA, KK), pp. 2014–2023.
ICML-2016-OswalCRRN #network #similarity: Representational Similarity Learning with Application to Brain Networks (UO, CRC, MALR, TTR, RDN), pp. 1041–1049.
ICML-2016-Papakonstantinou #on the: On the Power and Limits of Distance-Based Learning (PAP, JX0, GY), pp. 2263–2271.
ICML-2016-PatriniNNC #robust: Loss factorization, weakly supervised learning and label noise robustness (GP, FN, RN, MC), pp. 708–717.
ICML-2016-RahmaniA #approach #composition #matrix #performance: A Subspace Learning Approach for High Dimensional Matrix Decomposition with Efficient Column/Row Sampling (MR, GKA), pp. 1206–1214.
ICML-2016-ScheinZBW #composition: Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations (AS, MZ, DMB, HMW), pp. 2810–2819.
ICML-2016-SchnabelSSCJ #evaluation #recommendation: Recommendations as Treatments: Debiasing Learning and Evaluation (TS, AS, AS, NC, TJ), pp. 1670–1679.
ICML-2016-ShahamCDJNCK #approach: A Deep Learning Approach to Unsupervised Ensemble Learning (US, XC, OD, AJ, BN, JTC, YK), pp. 30–39.
ICML-2016-ShahG #correlation: Pareto Frontier Learning with Expensive Correlated Objectives (AS, ZG), pp. 1919–1927.
ICML-2016-SinglaTK #elicitation: Actively Learning Hemimetrics with Applications to Eliciting User Preferences (AS, ST, AK0), pp. 412–420.
ICML-2016-SongGC #network #sequence: Factored Temporal Sigmoid Belief Networks for Sequence Learning (JS, ZG, LC), pp. 1272–1281.
ICML-2016-SuLCC #modelling #statistics #visual notation: Nonlinear Statistical Learning with Truncated Gaussian Graphical Models (QS, XL, CC, LC), pp. 1948–1957.
ICML-2016-SunVBB #predict: Learning to Filter with Predictive State Inference Machines (WS0, AV, BB, JAB), pp. 1197–1205.
ICML-2016-SyrgkanisKS #algorithm #performance: Efficient Algorithms for Adversarial Contextual Learning (VS, AK, RES), pp. 2159–2168.
ICML-2016-ThomasB #evaluation #policy: Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning (PST, EB), pp. 2139–2148.
ICML-2016-UstinovskiyFGS: Meta-Gradient Boosted Decision Tree Model for Weight and Target Learning (YU, VF, GG, PS), pp. 2692–2701.
ICML-2016-WangSHHLF #architecture #network: Dueling Network Architectures for Deep Reinforcement Learning (ZW0, TS, MH, HvH, ML, NdF), pp. 1995–2003.
ICML-2016-XieZX #modelling: Diversity-Promoting Bayesian Learning of Latent Variable Models (PX, JZ0, EPX), pp. 59–68.
ICML-2016-XuFZ #process: Learning Granger Causality for Hawkes Processes (HX, MF, HZ), pp. 1717–1726.
ICML-2016-YangCS #graph: Revisiting Semi-Supervised Learning with Graph Embeddings (ZY, WWC, RS), pp. 40–48.
ICML-2016-YangZJY #online: Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient (TY, LZ0, RJ, JY), pp. 449–457.
ICML-2016-YaoK #performance #product line: Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity (QY, JTK), pp. 2645–2654.
ICML-2016-YuL #multi #performance: Learning from Multiway Data: Simple and Efficient Tensor Regression (RY, YL0), pp. 373–381.
ICML-2016-ZadehHS #geometry #metric: Geometric Mean Metric Learning (PZ, RH, SS), pp. 2464–2471.
ICML-2016-ZarembaMJF #algorithm: Learning Simple Algorithms from Examples (WZ, TM, AJ, RF), pp. 421–429.
ICML-2016-ZhaoPX #modelling: Learning Mixtures of Plackett-Luce Models (ZZ, PP, LX), pp. 2906–2914.
ICPR-2016-AbdicFBARMS #approach #detection: Detecting road surface wetness from audio: A deep learning approach (IA, LF, DEB, WA, BR, EM, BWS), pp. 3458–3463.
ICPR-2016-AfridiRS #framework #latency #named: L-CNN: Exploiting labeling latency in a CNN learning framework (MJA, AR, EMS), pp. 2156–2161.
ICPR-2016-AgustssonTG: Regressor Basis Learning for anchored super-resolution (EA, RT, LVG), pp. 3850–3855.
ICPR-2016-AhmedK #multi #taxonomy: Coupled multiple dictionary learning based on edge sharpness for single-image super-resolution (JA, RK), pp. 3838–3843.
ICPR-2016-BalaziaS16a #recognition #robust: Learning robust features for gait recognition by Maximum Margin Criterion (MB, PS), pp. 901–906.
ICPR-2016-BarddalGGBE #nearest neighbour: Overcoming feature drifts via dynamic feature weighted k-nearest neighbor learning (JPB, HMG, JG, AdSBJ, FE), pp. 2186–2191.
ICPR-2016-BayramogluKH #classification #image #independence: Deep learning for magnification independent breast cancer histopathology image classification (NB, JK, JH), pp. 2440–2445.
ICPR-2016-BorgaAL #image #segmentation: Semi-supervised learning of anatomical manifolds for atlas-based segmentation of medical images (MB, TA, ODL), pp. 3146–3149.
ICPR-2016-CaoN #fine-grained #process #recognition: Exploring deep learning based solutions in fine grained activity recognition in the wild (SC, RN), pp. 384–389.
ICPR-2016-CarbonneauGG #identification #multi #random #using: Witness identification in multiple instance learning using random subspaces (MAC, EG, GG), pp. 3639–3644.
ICPR-2016-ChenWHF #detection #estimation: Deep learning for integrated hand detection and pose estimation (TYC, MYW, YHH, LCF), pp. 615–620.
ICPR-2016-ChenZW #approach #network #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-DasguptaYO #sequence: Regularized dynamic Boltzmann machine with Delay Pruning for unsupervised learning of temporal sequences (SD, TY, TO), pp. 1201–1206.
ICPR-2016-DevanneWDBBP #analysis: Learning shape variations of motion trajectories for gait analysis (MD, HW, MD, SB, ADB, PP), pp. 895–900.
ICPR-2016-FanWH #adaptation #multi: Multi-stage multi-task feature learning via adaptive threshold (YF, YW, TZH), pp. 1666–1671.
ICPR-2016-FengLL #effectiveness #using: Learning effective Gait features using LSTM (YF0, YL, JL), pp. 325–330.
ICPR-2016-Forstner #modelling #semantics: A future for learning semantic models of man-made environments (WF), pp. 2475–2485.
ICPR-2016-GhaderiA #network: Selective unsupervised feature learning with Convolutional Neural Network (S-CNN) (AG, VA), pp. 2486–2490.
ICPR-2016-GonzalezVT #classification #invariant: Learning rotation invariant convolutional filters for texture classification (DM, MV, DT), pp. 2012–2017.
ICPR-2016-GuoCL #multi: Multi-label learning with globAl densiTy fusiOn Mapping features (YG, FC, GL0), pp. 462–467.
ICPR-2016-HoSSEA #approach #estimation #parametricity: A temporal deep learning approach for MR perfusion parameter estimation in stroke (KCH, FS, KVS, SES, CWA), pp. 1315–1320.
ICPR-2016-HouXX0 #classification #graph: Semi-supervised learning competence of classifiers based on graph for dynamic classifier selection (CH, YX, ZX, JS0), pp. 3650–3654.
ICPR-2016-HuangWLLBC #automation #clustering #estimation #parametricity: Ensemble-driven support vector clustering: From ensemble learning to automatic parameter estimation (DH, CDW, JHL, YL0, SB, YC), pp. 444–449.
ICPR-2016-HuLL #named #representation #semantics #video: Video2vec: Learning semantic spatio-temporal embeddings for video representation (ShH, YL, BL), pp. 811–816.
ICPR-2016-JafariKNSSWN #image #segmentation #using: Skin lesion segmentation in clinical images using deep learning (MHJ, NK, ENE, SS, SMRS, KRW, KN), pp. 337–342.
ICPR-2016-JenckelBD #documentation #named #sequence: anyOCR: A sequence learning based OCR system for unlabeled historical documents (MJ, SSB, AD0), pp. 4035–4040.
ICPR-2016-JiaoZ #multi #taxonomy #using: Multiple Instance Dictionary Learning using Functions of Multiple Instances (CJ, AZ), pp. 2688–2693.
ICPR-2016-JohnKGNMI #modelling #performance #segmentation #using: Fast road scene segmentation using deep learning and scene-based models (VJ, KK, CG, HTN, SM, KI), pp. 3763–3768.
ICPR-2016-KalraSRT #network #using: Learning opposites using neural networks (SK, AS, SR, HRT), pp. 1213–1218.
ICPR-2016-KanehiraSH #multi #scalability: True-negative label selection for large-scale multi-label learning (AK, AS, TH), pp. 3673–3678.
ICPR-2016-KaremF #concept #multi: Multiple Instance Learning with multiple positive and negative target concepts (AK, HF), pp. 474–479.
ICPR-2016-KaurDCM #hybrid #image: Hybrid deep learning for Reflectance Confocal Microscopy skin images (PK, KJD, GOC, MCM), pp. 1466–1471.
ICPR-2016-KawanishiDIMF #classification #robust: Misclassification tolerable learning for robust pedestrian orientation classification (YK, DD, II, HM, HF), pp. 486–491.
ICPR-2016-KhanH #adaptation #polynomial #using: Adapting instance weights for unsupervised domain adaptation using quadratic mutual information and subspace learning (MNAK, DRH), pp. 1560–1565.
ICPR-2016-KhodabandehMVMP #segmentation #video: Unsupervised learning of supervoxel embeddings for video Segmentation (MK, SM, AV, NM, EMP, SS, GM), pp. 2392–2397.
ICPR-2016-Kobayashi #data-driven #image #similarity: Learning data-driven image similarity measure (TK), pp. 3679–3684.
ICPR-2016-LangenkamperN #architecture #classification #detection #online #realtime: COATL - a learning architecture for online real-time detection and classification assistance for environmental data (DL, TWN), pp. 597–602.
ICPR-2016-LiangSWMWSG #image #optimisation #performance #precise #retrieval #similarity: Optimizing top precision performance measure of content-based image retrieval by learning similarity function (RZL, LS, HW, JM, JJYW, QS, YG), pp. 2954–2958.
ICPR-2016-LiaoQL #image #multi: Semisupervised manifold learning for color transfer between multiview images (DL, YQ, ZNL), pp. 259–264.
ICPR-2016-Liu16a #classification #multi #network #scalability: Hierarchical learning for large multi-class network classification (LL), pp. 2307–2312.
ICPR-2016-LoogY #consistency #empirical #nondeterminism: An empirical investigation into the inconsistency of sequential active learning (ML, YY), pp. 210–215.
ICPR-2016-MaoZCLHY16a #collaboration #recognition #taxonomy: Group and collaborative dictionary pair learning for face recognition (MM, ZZ, ZC, HL, XH, RY), pp. 4107–4111.
ICPR-2016-MarkusPA #optimisation: Learning local descriptors by optimizing the keypoint-correspondence criterion (NM, ISP, JA), pp. 2380–2385.
ICPR-2016-MoutafisLK #metric: Regression-based metric learning (PM, ML, IAK), pp. 2700–2705.
ICPR-2016-NahaW16a #segmentation #using: Object figure-ground segmentation using zero-shot learning (SN, YW0), pp. 2842–2847.
ICPR-2016-Nilsson #consistency #taxonomy: Sparse coding with unity range codes and label consistent discriminative dictionary learning (MN), pp. 3186–3191.
ICPR-2016-NogueiraMCSS #image #semantics: Learning to semantically segment high-resolution remote sensing images (KN, MDM, JC, WRS, JAdS), pp. 3566–3571.
ICPR-2016-OhY #algorithm #graph: Enhancing label inference algorithms considering vertex importance in graph-based semi-supervised learning (BO, JY), pp. 1671–1676.
ICPR-2016-OrriteRM #distance #process #sequence #using: One-shot learning of temporal sequences using a distance dependent Chinese Restaurant Process (CO, MR, CM), pp. 2694–2699.
ICPR-2016-PalCGCC #multi #using: Severity grading of psoriatic plaques using deep CNN based multi-task learning (AP, AC, UG, AC, RC), pp. 1478–1483.
ICPR-2016-PassalisT #embedded #retrieval #word: Bag of Embedded Words learning for text retrieval (NP, AT), pp. 2416–2421.
ICPR-2016-PengRP #network #recognition #using: Learning face recognition from limited training data using deep neural networks (XP, NKR, SP), pp. 1442–1447.
ICPR-2016-PironkovDD #automation #multi #recognition #speech: Speaker-aware Multi-Task Learning for automatic speech recognition (GP, SD, TD), pp. 2900–2905.
ICPR-2016-QianCKNM: Deep structured-output regression learning for computational color constancy (YQ, KC0, JKK, JN, JM), pp. 1899–1904.
ICPR-2016-QuachtranHS #detection #using: Detection of Intracranial Hypertension using Deep Learning (BQ, RBH, FS), pp. 2491–2496.
ICPR-2016-QuLFT #effectiveness #retrieval: Improving PGF retrieval effectiveness with active learning (JQ, XL, SF, ZT), pp. 1125–1130.
ICPR-2016-RaytchevKKTK: Ensemble-based local learning for high-dimensional data regression (BR, YK, MK, TT, KK), pp. 2640–2645.
ICPR-2016-RedkoB #kernel: Kernel alignment for unsupervised transfer learning (IR, YB), pp. 525–530.
ICPR-2016-RotaSCP #analysis #education #forensics #image #question #student: Bad teacher or unruly student: Can deep learning say something in Image Forensics analysis? (PR, ES, VC, CP), pp. 2503–2508.
ICPR-2016-RoyTL #network: Context-regularized learning of fully convolutional networks for scene labeling (AR, ST, LJL), pp. 3751–3756.
ICPR-2016-Saha0PV #problem #visual notation: Transfer learning for rare cancer problems via Discriminative Sparse Gaussian Graphical model (BS, SG0, DQP, SV), pp. 537–542.
ICPR-2016-SaikiaSSKG #analysis #kernel #multi #using: Multiple kernel learning using data envelopment analysis and feature vector selection and projection (GS, SS, VVS, RDK, PG), pp. 520–524.
ICPR-2016-ShankarDG #network: Reinforcement Learning via Recurrent Convolutional Neural Networks (TS, SKD, PG), pp. 2592–2597.
ICPR-2016-ShwetaE0B #architecture #identification #interactive: A deep learning architecture for protein-protein Interaction Article identification (S, AE, SS0, PB), pp. 3128–3133.
ICPR-2016-SoleymaniGF: Loss factors for learning Boosting ensembles from imbalanced data (RS, EG, GF), pp. 204–209.
ICPR-2016-SousaB #consistency: Constrained Local and Global Consistency for semi-supervised learning (CARdS, GEAPAB), pp. 1689–1694.
ICPR-2016-SouzaSC #comprehension #semantics: Building semantic understanding beyond deep learning from sound and vision (FDMdS, SS, GCC), pp. 2097–2102.
ICPR-2016-SunBTTH #detection #locality #using: Tattoo detection and localization using region-based deep learning (ZS, JB, PT, MT, AH), pp. 3055–3060.
ICPR-2016-SunHLK #multi #network #recognition: Multiple Instance Learning Convolutional Neural Networks for object recognition (MS, TXH, MCL, AKR), pp. 3270–3275.
ICPR-2016-SvobodaMB #recognition: Palmprint recognition via discriminative index learning (JS, JM, MMB), pp. 4232–4237.
ICPR-2016-TairaTO #robust #synthesis: Robust feature matching by learning descriptor covariance with viewpoint synthesis (HT, AT, MO), pp. 1953–1958.
ICPR-2016-TounsiMA #framework #recognition #taxonomy: Supervised dictionary learning in BoF framework for Scene Character recognition (MT, IM, AMA), pp. 3987–3992.
ICPR-2016-Triantafyllidou #detection #incremental #network: Face detection based on deep convolutional neural networks exploiting incremental facial part learning (DT, AT), pp. 3560–3565.
ICPR-2016-TzelepiT #image #retrieval: Exploiting supervised learning for finetuning deep CNNs in content based image retrieval (MT, AT), pp. 2918–2923.
ICPR-2016-UlmB: Learning tubes (MU, NB), pp. 3655–3660.
ICPR-2016-WangHG #classification #novel: A novel fingerprint classification method based on deep learning (RW, CH, TG), pp. 931–936.
ICPR-2016-WangLLCL #visual notation: Visual tracking via sparsity pattern learning (YW, YL0, ZL, LFC, HL), pp. 2716–2721.
ICPR-2016-WangZWGSH #identification #metric #similarity: Contextual Similarity Regularized Metric Learning for person re-identification (JW0, JZ, ZW, CG, NS, RH0), pp. 2048–2053.
ICPR-2016-WeiLSKM #taxonomy: Joint learning dictionary and discriminative features for high dimensional data (XW, YL, HS, MK, YLM), pp. 366–371.
ICPR-2016-WichtFH #keyword: Deep learning features for handwritten keyword spotting (BW, AF0, JH), pp. 3434–3439.
ICPR-2016-WuWJ #multi #recognition: Multiple Facial Action Unit recognition by learning joint features and label relations (SW, SW, QJ), pp. 2246–2251.
ICPR-2016-XueB #multi: Multi-task learning for one-class SVM with additional new features (YX, PB), pp. 1571–1576.
ICPR-2016-XuSARS #multi #recognition #retrieval #taxonomy: Multi-Paced Dictionary Learning for cross-domain retrieval and recognition (DX0, JS, XAP, ER0, NS), pp. 3228–3233.
ICPR-2016-XuT #3d #network: Beam search for learning a deep Convolutional Neural Network of 3D shapes (XX, ST), pp. 3506–3511.
ICPR-2016-YangJPL #image #taxonomy: Enhancement of Low Light Level Images with coupled dictionary learning (JY, XJ, CP, CLL), pp. 751–756.
ICPR-2016-YangL #nondeterminism #using: Active learning using uncertainty information (YY, ML), pp. 2646–2651.
ICPR-2016-ZhaoZWJ #multi: Multilingual articulatory features augmentation learning (YZ, RZ, XW0, QJ), pp. 2895–2899.
ICPR-2016-ZhengYYY #feature model #robust: Robust unsupervised feature selection by nonnegative sparse subspace learning (WZ, HY, JY0, JY), pp. 3615–3620.
ICPR-2016-ZhuWLZ #gender #lightweight #network #recognition: Learning a lightweight deep convolutional network for joint age and gender recognition (LZ, KW, LL, LZ0), pp. 3282–3287.
KDD-2016-BorisyukKSZ #documentation #framework #modelling #named #query: CaSMoS: A Framework for Learning Candidate Selection Models over Structured Queries and Documents (FB, KK, DS, BZ), pp. 441–450.
KDD-2016-ChangZTYCHH #network #streaming: Positive-Unlabeled Learning in Streaming Networks (SC, YZ0, JT, DY, YC, MAHJ, TSH), pp. 755–764.
KDD-2016-ChoiBSCTBTS #concept #multi #representation: Multi-layer Representation Learning for Medical Concepts (EC, MTB, ES, CC, MT, JB, JTS, JS), pp. 1495–1504.
KDD-2016-FeiW0 #cumulative #information management: Learning Cumulatively to Become More Knowledgeable (GF, SW, BL0), pp. 1565–1574.
KDD-2016-Freitas #composition #network: Learning to Learn and Compositionality with Deep Recurrent Neural Networks: Learning to Learn and Compositionality (NdF), p. 3.
KDD-2016-GroverL #named #network #scalability: node2vec: Scalable Feature Learning for Networks (AG, JL), pp. 855–864.
KDD-2016-Herbrich #modelling #scalability: Learning Sparse Models at Scale (RH), p. 407.
KDD-2016-HuoNH #effectiveness #metric #robust #using: Robust and Effective Metric Learning Using Capped Trace Norm: Metric Learning via Capped Trace Norm (ZH, FN, HH), pp. 1605–1614.
KDD-2016-LiGHZ #recommendation: Point-of-Interest Recommendations: Learning Potential Check-ins from Friends (HL, YG, RH, HZ), pp. 975–984.
KDD-2016-LiMLFDYLQ #big data #data analysis #performance #scalability #taxonomy: Scalable Fast Rank-1 Dictionary Learning for fMRI Big Data Analysis (XL0, MM, BL, MSF, ID, JY, TL, SQ), pp. 511–519.
KDD-2016-LinXBJZ #feature model #interactive #multi: Multi-Task Feature Interaction Learning (KL, JX, IMB, SJ, JZ), pp. 1735–1744.
KDD-2016-LiWYR #analysis #multi: A Multi-Task Learning Formulation for Survival Analysis (YL, JW0, JY, CKR), pp. 1715–1724.
KDD-2016-LynchAA #image #multimodal #rank #scalability #semantics #visual notation: Images Don't Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank (CL, KA, JA), pp. 541–548.
KDD-2016-NingMRR #modelling #multi: Modeling Precursors for Event Forecasting via Nested Multi-Instance Learning (YN, SM, HR, NR), pp. 1095–1104.
KDD-2016-PetitjeanW #modelling #scalability #visual notation: Scalable Learning of Graphical Models (FP, GIW), pp. 2131–2132.
KDD-2016-ReddyLBJ #bound #scheduling: Unbounded Human Learning: Optimal Scheduling for Spaced Repetition (SR, IL, SB, TJ), pp. 1815–1824.
KDD-2016-Schneider #embedded #optimisation: Bayesian Optimization and Embedded Learning Systems (JS), p. 413.
KDD-2016-XuT0 #multi #robust: Robust Extreme Multi-label Learning (CX0, DT, CX0), pp. 1275–1284.
KDD-2016-ZhaiCZZ #named #network #online: DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks (SZ, KhC, RZ, Z(Z), pp. 1295–1304.
KDD-2016-ZhangYS #online #symmetry: Online Asymmetric Active Learning with Imbalanced Data (XZ, TY, PS), pp. 2055–2064.
KDD-2016-ZhangZL #ambiguity: Partial Label Learning via Feature-Aware Disambiguation (MLZ, BBZ, XYL), pp. 1335–1344.
KDD-2016-ZhangZWX: Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising (WZ0, TZ, JW0, JX), pp. 665–674.
KDD-2016-ZhaoYCLR #multi: Hierarchical Incomplete Multi-source Feature Learning for Spatiotemporal Event Forecasting (LZ0, JY, FC0, CTL, NR), pp. 2085–2094.
KDD-2016-ZhengYC #invariant #performance #taxonomy: Efficient Shift-Invariant Dictionary Learning (GZ, YY, JGC), pp. 2095–2104.
MoDELS-2016-BatotS #framework #testing #unification: A generic framework for model-set selection for the unification of testing and learning MDE tasks (EB, HAS), pp. 374–384.
PLDI-2016-HeuleS0A #automation #set #synthesis: Stratified synthesis: automatically learning the x86-64 instruction set (SH, ES, RS0, AA), pp. 237–250.
PLDI-2016-ZhuPJ #automation #specification: Automatically learning shape specifications (HZ0, GP, SJ), pp. 491–507.
POPL-2016-0001NMR #invariant #using: Learning invariants using decision trees and implication counterexamples (PG0, DN, PM, DR), pp. 499–512.
POPL-2016-LongR #automation #generative: Automatic patch generation by learning correct code (FL, MR), pp. 298–312.
POPL-2016-RaychevBVK #semistructured data #source code: Learning programs from noisy data (VR, PB, MTV, AK0), pp. 761–774.
SAS-2016-HeoOY #clustering #static analysis: Learning a Variable-Clustering Strategy for Octagon from Labeled Data Generated by a Static Analysis (KH, HO, HY), pp. 237–256.
ASE-2016-ChenCXX #retrieval: Learning a dual-language vector space for domain-specific cross-lingual question retrieval (GC, CC, ZX, BX), pp. 744–755.
ASE-2016-KrishnaMF #automation: Too much automation? the bellwether effect and its implications for transfer learning (RK, TM, WF), pp. 122–131.
ASE-2016-QiJZWC #estimation #obfuscation #privacy #subclass: Privacy preserving via interval covering based subclass division and manifold learning based bi-directional obfuscation for effort estimation (FQ, XYJ, XZ, FW, LC), pp. 75–86.
ASE-2016-WhiteTVP #clone detection #detection: Deep learning code fragments for code clone detection (MW, MT, CV, DP), pp. 87–98.
FSE-2016-BusjaegerX #case study #industrial: Learning for test prioritization: an industrial case study (BB, TX), pp. 975–980.
FSE-2016-GuZZK #api: Deep API learning (XG, HZ0, DZ, SK0), pp. 631–642.
FSE-2016-NguyenHCNMRND #api #fine-grained #recommendation #statistics #using: API code recommendation using statistical learning from fine-grained changes (ATN0, MH, MC, HAN, LM, ER, TNN, DD), pp. 511–522.
ICSE-2016-NguyenPVN #api #approach #bytecode #statistics: Learning API usages from bytecode: a statistical approach (TTN, HVP, PMV, TTN), pp. 416–427.
ICSE-2016-WangLT #automation #fault #predict #semantics: Automatically learning semantic features for defect prediction (SW0, TL, LT0), pp. 297–308.
CASE-2016-LangsfeldKKG #modelling #online: Robotic bimanual cleaning of deformable objects with online learning of part and tool models (JDL, AMK, KNK, SKG), pp. 626–632.
CASE-2016-LaskeyLCGHPDG #using: Robot grasping in clutter: Using a hierarchy of supervisors for learning from demonstrations (ML, JL, CC, DVG, WYSH, FTP, ADD, KG), pp. 827–834.
CASE-2016-LiuS #kernel #online #recognition #taxonomy: Online kernel dictionary learning for object recognition (HL0, FS), pp. 268–273.
CAV-2016-Fiterau-Brostean #implementation #model checking: Combining Model Learning and Model Checking to Analyze TCP Implementations (PFB, RJ, FWV), pp. 454–471.
CAV-2016-SantolucitoZP #automation #probability: Probabilistic Automated Language Learning for Configuration Files (MS, EZ, RP), pp. 80–87.
CSL-2016-Silva #algebra: Coalgebraic Learning (AS0), p. 1.
ICTSS-2016-ReichstallerEKR #testing #using: Risk-Based Interoperability Testing Using Reinforcement Learning (AR, BE, AK, WR, MG), pp. 52–69.
ECSA-2015-KiwelekarW #architecture: Learning Objectives for a Course on Software Architecture (AWK, HSW), pp. 169–180.
DRR-2015-FuLLQT #diagrams #multi #retrieval: A diagram retrieval method with multi-label learning (SF, XL, LL, JQ, ZT).
HT-2015-KirchnerR #collaboration #in the cloud: Collaborative Learning in the Cloud: A Cross-Cultural Perspective of Collaboration (KK, LR), pp. 333–336.
HT-2015-MishraDBS #analysis #incremental #sentiment: Sentiment Analysis with Incremental Human-in-the-Loop Learning and Lexical Resource Customization (SM, JD, JB, ES), pp. 323–325.
JCDL-2015-KananZMF #big data #problem #summary: Big Data Text Summarization for Events: A Problem Based Learning Course (TK, XZ, MM, EAF), pp. 87–90.
SIGMOD-2015-KumarNP #linear #modelling #normalisation: Learning Generalized Linear Models Over Normalized Data (AK, JFN, JMP), pp. 1969–1984.
VLDB-2015-QianGJ #adaptation #comparison: Learning User Preferences By Adaptive Pairwise Comparison (LQ, JG, HVJ), pp. 1322–1333.
EDM-2015-BergnerKP #analysis #challenge: Methodological Challenges in the Analysis of MOOC Data for Exploring the Relationship between Discussion Forum Views and Learning Outcomes (YB, DK, DEP), pp. 234–241.
EDM-2015-BhatnagarDWLDLC #analysis: An Analysis of Peer-submitted and Peer-reviewed Answer Rationales in a Web-based Peer Instruction Based Learning Environment (SB, MCD, CW, NL, MD, KL, ESC), pp. 456–459.
EDM-2015-BumbacherSWB #behaviour #comprehension #concept #development #how #physics: Learning Environments and Inquiry Behaviors in Science Inquiry Learning: How Their Interplay Affects the Development of Conceptual Understanding in Physics (EB, SS, MW, PB), pp. 61–68.
EDM-2015-ChandrasekaranK: Learning Instructor Intervention from MOOC Forums: Early Results and Issues (MKC, MYK, BCYT, KR), pp. 218–225.
EDM-2015-ChenBD #detection: Video-Based Affect Detection in Noninteractive Learning Environments (YC, NB, SKD), pp. 440–443.
EDM-2015-DoroudiHAB #comprehension #how #induction #refinement #robust #towards: Towards Understanding How to Leverage Sense-making, Induction/Refinement and Fluency to Improve Robust Learning (SD, KH, VA, EB), pp. 376–379.
EDM-2015-Fancsali #algebra #behaviour #modelling #using #visual notation: Confounding Carelessness? Exploring Causal Relationships Between Carelessness, Affect, Behavior, and Learning in Cognitive Tutor Algebra Using Graphical Causal Models (SF), pp. 508–511.
EDM-2015-JugoKS #optimisation #tool support #visual notation: Integrating a Web-based ITS with DM tools for Providing Learning Path Optimization and Visual Analytics (IJ, BK, VS), pp. 574–575.
EDM-2015-KeshtkarCKC #interactive #student: Analyzing Students' Interaction Based on Their Response to Determine Their Learning Outcomes (FK, JC, BK, AC), pp. 588–589.
EDM-2015-LewkowZRE #education #framework #platform #scalability #streaming #towards: Learning Analytics Platform. Towards an Open Scalable Streaming Solution for Education (NL, NLZ, MR, AE), pp. 460–463.
EDM-2015-LiuK #clustering #fault #student: Variations in Learning Rate: Student Clustering Based on Systematic Residual Error Patterns Across Practice Opportunities (RL0, KRK), pp. 420–423.
EDM-2015-MacLellanLK #modelling #student: Accounting for Slipping and Other False Negatives in Logistic Models of Student Learning (CJM, RL0, KRK), pp. 53–60.
EDM-2015-MostowGEG #automation #identification #word: Automatic Identification of Nutritious Contexts for Learning Vocabulary Words (JM, DG, RE, RG), pp. 266–273.
EDM-2015-Olivares-Rodriguez #mining #student #word: Learning the Creative Potential of Students by Mining a Word Association Task (COR, MG), pp. 400–403.
EDM-2015-OlsenAR #collaboration #performance #predict #student: Predicting Student Performance In a Collaborative Learning Environment (JKO, VA, NR), pp. 211–217.
EDM-2015-Ostrow #adaptation #motivation #student: Enhancing Student Motivation and Learning Within Adaptive Tutors (KO), pp. 668–670.
EDM-2015-Pedro #student: Assessing the Roles of Student Engagement and Academic Emotions within Middle School Computer-Based Learning in College-Going Pathways (MOSP), pp. 656–658.
EDM-2015-Pelanek15b #modelling #question #student: Modeling Student Learning: Binary or Continuous Skill? (RP), pp. 560–561.
EDM-2015-Rasanen #education: Educational Neuroscience as a Tool to Understand Learning and Learning Disabilities in Mathematics (PR), p. 7.
EDM-2015-Rau #equation #how #why: Why Do the Rich Get Richer? A Structural Equation Model to Test How Spatial Skills Affect Learning with Representations (MAR), pp. 350–357.
EDM-2015-RitterF: Carnegie Learning's Cognitive Tutor (SR, SF), pp. 633–634.
EDM-2015-RoweBA #game studies: Strategic Game Moves Mediate Implicit Science Learning (ER, RSB, JAC), pp. 432–435.
EDM-2015-SiemensBG #graph: Personal Knowledge/Learning Graph (GS, RSB, DG), p. 5.
EDM-2015-Streeter #modelling: Mixture Modeling of Individual Learning Curves (MJS), pp. 45–52.
EDM-2015-Tibbles #data mining #mining: Exploring the Impact of Spacing in Mathematics Learning through Data Mining (RT), pp. 590–591.
EDM-2015-Truong #adaptation: Integrating Learning Styles into Adaptive e-Learning System (HMT), pp. 645–647.
EDM-2015-VossSMS #approach #dataset #matrix: A Transfer Learning Approach for Applying Matrix Factorization to Small ITS Datasets (LV, CS, CM, LST), pp. 372–375.
EDM-2015-WangYWKR #behaviour #how #student: Investigating How Student's Cognitive Behavior in MOOC Discussion Forum Affect Learning Gains (XW0, DY, MW, KRK, CPR), pp. 226–233.
EDM-2015-YeKSB #behaviour #multi #process #sequence: Learning Behavior Characterization with Multi-Feature, Hierarchical Activity Sequences (CY, JSK, JRS, GB), pp. 380–383.
EDM-2015-ZhengVP #composition #performance: The Impact of Small Learning Group Composition on Drop-Out Rate and Learning Performance in a MOOC (ZZ, TV, NP), pp. 500–503.
ITiCSE-2015-AlshammariAH #adaptation #education #security: The Impact of Learning Style Adaptivity in Teaching Computer Security (MA, RA, RJH), pp. 135–140.
ITiCSE-2015-Annamaa #ide #programming #python: Thonny, : a Python IDE for Learning Programming (AA), p. 343.
ITiCSE-2015-Cukierman #predict #process #student: Predicting Success in University First Year Computing Science Courses: The Role of Student Participation in Reflective Learning Activities and in I-clicker Activities (DC), pp. 248–253.
ITiCSE-2015-Hamilton #education: Learning and Teaching Computing Sustainability (MH), p. 338.
ITiCSE-2015-Harms #community #source code: Department Programs to Encourage and Support Service Learning and Community Engagement (DEH), p. 330.
ITiCSE-2015-MartinezGB #comparison #concept #framework #multi #platform #programming: A Comparison of Preschool and Elementary School Children Learning Computer Science Concepts through a Multilanguage Robot Programming Platform (MCM, MJG, LB), pp. 159–164.
ITiCSE-2015-QuinsonO #education #programming: A Teaching System to Learn Programming: the Programmer’s Learning Machine (MQ, GO), pp. 260–265.
ITiCSE-2015-SantosSFN #agile #development #framework #mobile: Combining Challenge-Based Learning and Scrum Framework for Mobile Application Development (ARS, AS, PF, MN), pp. 189–194.
ITiCSE-2015-SettleLS #community: A Computer Science Linked-courses Learning Community (AS, JL, TS), pp. 123–128.
ITiCSE-2015-TarmazdiVSFF #using #visualisation: Using Learning Analytics to Visualise Computer Science Teamwork (HT, RV, CS, KEF, NJGF), pp. 165–170.
ITiCSE-2015-Tudor #optimisation #query #xml: Virtual Learning Laboratory about Query Optimization against XML Data (LNT), p. 348.
SIGITE-2015-BradyWGAW #low cost #programmable #smarttech: The CCL-Parallax Programmable Badge: Learning with Low-Cost, Communicative Wearable Computers (CEB, DW, KG, GA, UW), pp. 139–144.
SIGITE-2015-Miller #evaluation #usability: Usability Evaluation: Learning When Method Findings Converge-And When They Don’t (CSM), pp. 167–172.
SIGITE-2015-MillerSL #object-oriented #programming #python #testing #towards: Learning Object-Oriented Programming in Python: Towards an Inventory of Difficulties and Testing Pitfalls (CSM, AS, JL), pp. 59–64.
SIGITE-2015-NicolaiNHW #education #industrial: Experiential Learning Business/Industry and Education Wants and Needs (BN, DN, CHJ, CW), pp. 95–96.
SIGITE-2015-SettleLS #community #development: Evaluating a Linked-courses Learning Community for Development Majors (AS, JL, TS), pp. 127–132.
ICSME-2015-CorleyDK #feature model #using: Exploring the use of deep learning for feature location (CSC, KD, NAK), pp. 556–560.
MSR-2015-WhiteVVP #repository #towards: Toward Deep Learning Software Repositories (MW, CV, MLV, DP), pp. 334–345.
LATA-2015-Yoshinaka #boolean grammar #grammar inference: Learning Conjunctive Grammars and Contextual Binary Feature Grammars (RY), pp. 623–635.
SEFM-2015-Muhlberg0DLP #source code #verification: Learning Assertions to Verify Linked-List Programs (JTM, DHW, MD, GL, FP), pp. 37–52.
ICFP-2015-ZhuNJ #refinement: Learning refinement types (HZ, AVN, SJ), pp. 400–411.
AIIDE-2015-UriarteO #automation #game studies #modelling: Automatic Learning of Combat Models for RTS Games (AU, SO), pp. 212–219.
CHI-PLAY-2015-CakirCAL #game studies: An Optical Brain Imaging Study on the Improvements in Mathematical Fluency from Game-based Learning (MPÇ, NAÇ, HA, FJL), pp. 209–219.
CHI-PLAY-2015-EagleRHBBAE #interactive #network: Measuring Implicit Science Learning with Networks of Player-Game Interactions (ME, ER, DH, RB, TB, JAC, TE), pp. 499–504.
CHI-PLAY-2015-HarpsteadA #analysis #design #education #empirical #game studies #using: Using Empirical Learning Curve Analysis to Inform Design in an Educational Game (EH, VA), pp. 197–207.
CIG-2015-DannZT #approach: An improved approach to reinforcement learning in Computer Go (MD, FZ, JT), pp. 169–176.
CIG-2015-DobreL #game studies #mining #online: Online learning and mining human play in complex games (MSD, AL), pp. 60–67.
CIG-2015-GlavinM #clustering #game studies: Learning to shoot in first person shooter games by stabilizing actions and clustering rewards for reinforcement learning (FGG, MGM), pp. 344–351.
CIG-2015-HuangW: Learning overtaking and blocking skills in simulated car racing (HHH, TW), pp. 439–445.
CIG-2015-IvanovoRZL #monte carlo: Combining Monte Carlo tree search and apprenticeship learning for capture the flag (JI, WLR, FZ, XL0), pp. 154–161.
CIG-2015-KamekoMT #game studies #generative: Learning a game commentary generator with grounded move expressions (HK, SM, YT), pp. 177–184.
CIG-2015-NetoJ #automation #elicitation #named: ACE-RL-Checkers: Improving automatic case elicitation through knowledge obtained by reinforcement learning in player agents (HCN, RMdSJ), pp. 328–335.
CIG-2015-QuiterioPM #approach #geometry: A reinforcement learning approach for the circle agent of geometry friends (JQ, RP, FSM), pp. 423–430.
CIG-2015-Yao #game studies #speech: Keynote speech I: Co-evolutionary learning in game-playing (XY0), p. 16.
FDG-2015-KaoH15a #game studies #named: Mazzy: A STEM Learning Game (DK, DFH).
FDG-2015-KaoH15b #game studies #using: Exploring the Construction, Play, Use of Virtual Identities in a STEM Learning Game (DK, DFH).
FDG-2015-PackardO #behaviour #metric #similarity: Learning Behavior form Demonstration in Minecraft via Symbolic Similarity Measures (BP, SO).
FDG-2015-Pirker #collaboration: Learning in Collaborative and Motivational Virtual Environments (JP).
FDG-2015-ShakerAS #modelling: Active Learning for Player Modeling (NS, MAZ, MS).
FDG-2015-SummervilleBMJ #data-driven #game studies #generative: The Learning of Zelda: Data-Driven Level Generation for Action Role Playing Games (AS, MB, MM, AJ).
VS-Games-2015-AsadipourDC #approach #game studies: A Game-Based Training Approach to Enhance Human Hand Motor Learning and Control Abilities (AA0, KD, AC), pp. 1–6.
VS-Games-2015-DiazDHS #development #game studies #multi #online #using #video: Explicit Fun, Implicit Learning in Multiplayer Online Battle Arenas: Methodological Proposal for Studying the Development of Cognitive Skills Using Commercial Video Games (CMCD, BD, HH, JWS), pp. 1–3.
VS-Games-2015-PanzoliPL #communication #game studies: Communication and Knowledge Sharing in an Immersive Learning Game (DP, CPL, PL), pp. 1–8.
VS-Games-2015-YohannisP #algorithm #gamification #sorting #visualisation: Sort Attack: Visualization and Gamification of Sorting Algorithm Learning (AY, YP), pp. 1–8.
CHI-2015-BerardR #assessment #human-computer #similarity #towards: The Transfer of Learning as HCI Similarity: Towards an Objective Assessment of the Sensory-Motor Basis of Naturalness (FB, ARC), pp. 1315–1324.
CHI-2015-DavisK #student: Investigating High School Students’ Perceptions of Digital Badges in Afterschool Learning (KD, EK), pp. 4043–4046.
CHI-2015-KardanC #adaptation #evaluation #interactive #simulation: Providing Adaptive Support in an Interactive Simulation for Learning: An Experimental Evaluation (SK, CC), pp. 3671–3680.
CHI-2015-Noble #self: Resilience Ex Machina: Learning a Complex Medical Device for Haemodialysis Self-Treatment (PJN), pp. 4147–4150.
CHI-2015-NoroozMJMF #approach #named #smarttech #visualisation: BodyVis: A New Approach to Body Learning Through Wearable Sensing and Visualization (LN, MLM, AJ, BM, JEF), pp. 1025–1034.
CHI-2015-ShovmanBSS #3d #interface: Twist and Learn: Interface Learning in 3DOF Exploration of 3D Scatterplots (MMS, JLB, AS, KCSB), pp. 313–316.
CHI-2015-StrohmayerCB #people: Exploring Learning Ecologies among People Experiencing Homelessness (AS, RC, MB), pp. 2275–2284.
CHI-2015-Walther-FranksS #design #game studies: Robots, Pancakes, and Computer Games: Designing Serious Games for Robot Imitation Learning (BWF, JS, PS, AH, MB, RM), pp. 3623–3632.
CHI-2015-YannierKH #effectiveness #game studies #physics #question #tablet: Learning from Mixed-Reality Games: Is Shaking a Tablet as Effective as Physical Observation? (NY, KRK, SEH), pp. 1045–1054.
CSCW-2015-CoetzeeLFHH #interactive #scalability: Structuring Interactions for Large-Scale Synchronous Peer Learning (DC, SL, AF, BH, MAH), pp. 1139–1152.
CSCW-2015-DornSS #collaboration: Piloting TrACE: Exploring Spatiotemporal Anchored Collaboration in Asynchronous Learning (BD, LBS, AS), pp. 393–403.
CSCW-2015-JiaWXRC #behaviour #online #privacy #process: Risk-taking as a Learning Process for Shaping Teen’s Online Information Privacy Behaviors (HJ, PJW, HX, MBR, JMC), pp. 583–599.
DHM-HM-2015-NishimuraK #case study: A Study on Learning Effects of Marking with Highlighter Pen (HN, NK), pp. 357–367.
DUXU-DD-2015-KremerL #design #experience #research #user interface: Learning from Experience Oriented Disciplines for User Experience Design — A Research Agenda (SK, UL), pp. 306–314.
DUXU-IXD-2015-BorgesonFKTR #energy #visualisation: Learning from Hourly Household Energy Consumption: Extracting, Visualizing and Interpreting Household Smart Meter Data (SB, JAF, JK, CWT, RR), pp. 337–345.
DUXU-IXD-2015-BorumBB #design #lessons learnt: Designing with Young Children: Lessons Learned from a Co-creation of a Technology-Enhanced Playful Learning Environment (NB, EPB, ALB), pp. 142–152.
DUXU-IXD-2015-Celi #experience #modelling #risk management #user interface: Application of Dashboards and Scorecards for Learning Models IT Risk Management: A User Experience (EC), pp. 153–165.
DUXU-UI-2015-BeltranUPSSSPCA #design #game studies: Inclusive Gaming Creation by Design in Formal Learning Environments: “Girly-Girls” User Group in No One Left Behind (MEB, YU, AP, CS, WS, BS, SdlRP, MFCU, MTA), pp. 153–161.
HCI-DE-2015-BakkeB #developer #proximity: The Closer the Better: Effects of Developer-User Proximity for Mutual Learning (SB, TB), pp. 14–26.
HCI-IT-2015-TadayonMGRZLGP #case study #interactive: Interactive Motor Learning with the Autonomous Training Assistant: A Case Study (RT, TLM, MG, PMRF, JZ, ML, MG, SP), pp. 495–506.
HIMI-IKC-2015-AraiTA #development: Development of a Learning Support System for Class Structure Mapping Based on Viewpoint (TA, TT, TA), pp. 285–293.
HIMI-IKC-2015-HasegawaD #approach #framework #platform #ubiquitous: A Ubiquitous Lecture Archive Learning Platform with Note-Centered Approach (SH, JD), pp. 294–303.
HIMI-IKC-2015-HayashiH #analysis #concept #process: Analysis of the Relationship Between Metacognitive Ability and Learning Activity with Kit-Build Concept Map (YH, TH), pp. 304–312.
HIMI-IKC-2015-Iwata #difference: Method to Generate an Operation Learning Support System by Shortcut Key Differences in Similar Software (HI), pp. 332–340.
HIMI-IKC-2015-KimitaMMNIS #education: Learning State Model for Value Co-Creative Education Services (KK, KM, SM, YN, TI, YS), pp. 341–349.
HIMI-IKC-2015-WatanabeTA #abstraction #development #source code: Development of a Learning Support System for Reading Source Code by Stepwise Abstraction (KW, TT, TA), pp. 387–394.
HIMI-IKD-2015-WinterSTMCSVS #question #student: Learning to Manage NextGen Environments: Do Student Controllers Prefer to Use Datalink or Voice? (AW, JS, YT, AM, SC, KS, KPLV, TZS), pp. 661–667.
LCT-2015-BoonbrahmKB #artificial reality #student #using: Using Augmented Reality Technology in Assisting English Learning for Primary School Students (SB, CK, PB), pp. 24–32.
LCT-2015-DuA #artificial reality #design #evaluation: Design and Evaluation of a Learning Assistant System with Optical Head-Mounted Display (OHMD) (XD, AA), pp. 75–86.
LCT-2015-FonsecaRVG #3d #education: From Formal to Informal 3D Learning. Assesment of Users in the Education (DF, ER, FV, ODG), pp. 460–469.
LCT-2015-GoelMTPSYD #collaboration #named #student: CATALYST: Technology-Assisted Collaborative and Experiential Learning for School Students (VG, UM, ST, RMP, KS, KY, OD), pp. 482–491.
LCT-2015-GonzalezHGS #interactive #student #tool support: Exploring Student Interactions: Learning Analytics Tools for Student Tracking (MÁCG, ÁHG, FJGP, MLSE), pp. 50–61.
LCT-2015-HoffmannPLSMJ #student: Enhancing the Learning Success of Engineering Students by Virtual Experiments (MH, LP, LL, KS, TM, SJ), pp. 394–405.
LCT-2015-KimAKW #game studies: H-Treasure Hunt: A Location and Object-Based Serious Game for Cultural Heritage Learning at a Historic Site (HK, SA, SK, WW), pp. 561–572.
LCT-2015-KimCD #artificial reality #simulation: The Learning Effect of Augmented Reality Training in a Computer-Based Simulation Environment (JHK, TC, WD), pp. 406–414.
LCT-2015-KlemkeKLS #education #game studies #mobile #multi: Transferring an Educational Board Game to a Multi-user Mobile Learning Game to Increase Shared Situational Awareness (RK, SK, HL, MS), pp. 583–594.
LCT-2015-LambropoulosMFK #design #experience #ontology: Ontological Design to Support Cognitive Plasticity for Creative Immersive Experience in Computer Aided Learning (NL, IM, HMF, IAK), pp. 261–270.
LCT-2015-OrehovackiB #game studies #programming #quality: Inspecting Quality of Games Designed for Learning Programming (TO, SB), pp. 620–631.
LCT-2015-RodriguezOD #hybrid #recommendation #repository #student: A Student-Centered Hybrid Recommender System to Provide Relevant Learning Objects from Repositories (PAR, DAO, NDD), pp. 291–300.
LCT-2015-ShimizuO #design #implementation #novel #word: Design and Implementation of Novel Word Learning System “Überall” (RS, KO), pp. 148–159.
LCT-2015-TamuraTHN #generative #wiki: Generating Quizzes for History Learning Based on Wikipedia Articles (YT, YT, YH, YIN), pp. 337–346.
LCT-2015-VielRTP #design #interactive #multi: Design Solutions for Interactive Multi-video Multimedia Learning Objects (CCV, KRHR, CACT, MdGCP), pp. 160–171.
LCT-2015-YusoffK #design #game studies #interactive #persuasion: Game Rhetoric: Interaction Design Model of Persuasive Learning for Serious Games (ZY, AK), pp. 644–654.
ICEIS-v1-2015-PecliGPMFTTDFCG #predict #problem #reduction: Dimensionality Reduction for Supervised Learning in Link Prediction Problems (AP, BG, CCP, CM, FF, FT, JT, MVD, SF, MCC, RRG), pp. 295–302.
ICEIS-v1-2015-RibeiroTWBE: A Learning Model for Intelligent Agents Applied to Poultry Farming (RR, MT, ALW, APB, FE), pp. 495–503.
ICEIS-v1-2015-SouzaBGBE #online: Applying Ensemble-based Online Learning Techniques on Crime Forecasting (AJdS, APB, HMG, JPB, FE), pp. 17–24.
CIKM-2015-BizidNBFD #identification #microblog #sequence: Identification of Microblogs Prominent Users during Events by Learning Temporal Sequences of Features (IB, NN, PB, SF, AD), pp. 1715–1718.
CIKM-2015-BuchA #approximate #string #using: Approximate String Matching by End-Users using Active Learning (LB, AA0), pp. 93–102.
CIKM-2015-CaoLX #graph #named: GraRep: Learning Graph Representations with Global Structural Information (SC, WL0, QX), pp. 891–900.
CIKM-2015-HaoZHM #data type #online #similarity: Learning Relative Similarity from Data Streams: Active Online Learning Approaches (SH, PZ, SCHH, CM), pp. 1181–1190.
CIKM-2015-HeLJ0 #graph: Learning to Represent Knowledge Graphs with Gaussian Embedding (SH, KL0, GJ, JZ0), pp. 623–632.
CIKM-2015-HongWW #classification #clustering: Clustering-based Active Learning on Sensor Type Classification in Buildings (DH, HW, KW), pp. 363–372.
CIKM-2015-JinLZHH #distributed #multi #online: Collaborating between Local and Global Learning for Distributed Online Multiple Tasks (XJ0, PL0, FZ, JH, QH), pp. 113–122.
CIKM-2015-JinZPDLH #classification #multi #semantics: Heterogeneous Multi-task Semantic Feature Learning for Classification (XJ0, FZ, SJP, CD, PL0, QH), pp. 1847–1850.
CIKM-2015-KangLHWNXP #rank #similarity: Cross-Modal Similarity Learning: A Low Rank Bilinear Formulation (CK, SL, YH, JW, WN, SX, CP), pp. 1251–1260.
CIKM-2015-KholghiSZN #case study #information management #query: External Knowledge and Query Strategies in Active Learning: a Study in Clinical Information Extraction (MK, LS, GZ, ANN), pp. 143–152.
CIKM-2015-LiuTL #matrix #multi #named #scalability: MF-Tree: Matrix Factorization Tree for Large Multi-Class Learning (LL, PNT, XL), pp. 881–890.
CIKM-2015-MetrikovPA #crowdsourcing #integration #rank: Aggregation of Crowdsourced Ordinal Assessments and Integration with Learning to Rank: A Latent Trait Model (PM, VP, JAA), pp. 1391–1400.
CIKM-2015-MishraH #clustering #multi #using: Learning Task Grouping using Supervised Task Space Partitioning in Lifelong Multitask Learning (MM, JH), pp. 1091–1100.
CIKM-2015-MunozTG #approach #ranking: A Soft Computing Approach for Learning to Aggregate Rankings (JAVM, RdST, MAG), pp. 83–92.
CIKM-2015-ShuL #adaptation: Transductive Domain Adaptation with Affinity Learning (LS, LJL), pp. 1903–1906.
CIKM-2015-TranNKGA #adaptation #rank #summary #timeline: Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events (TT0, CN, NK, UG, AA), pp. 1201–1210.
CIKM-2015-WangSL #distance #summary #using: Update Summarization using Semi-Supervised Learning Based on Hellinger Distance (DW0, SS, TL0), pp. 1907–1910.
CIKM-2015-WanLKYGCH #classification #network: Classification with Active Learning and Meta-Paths in Heterogeneous Information Networks (CW, XL, BK, XY, QG, DWLC, JH0), pp. 443–452.
CIKM-2015-YeZMJZ #approach #consistency #multi #privacy #rank: Rank Consistency based Multi-View Learning: A Privacy-Preserving Approach (HJY, DCZ, YM, YJ0, ZHZ), pp. 991–1000.
CIKM-2015-YinWW #clustering #multi: Incomplete Multi-view Clustering via Subspace Learning (QY, SW, LW0), pp. 383–392.
CIKM-2015-ZenginC #documentation #topic: Learning User Preferences for Topically Similar Documents (MZ, BC), pp. 1795–1798.
CIKM-2015-ZhangJRXCY #graph #modelling #query: Learning Entity Types from Query Logs via Graph-Based Modeling (JZ, LJ, AR, SX, YC, PSY), pp. 603–612.
ECIR-2015-HuynhHR #analysis #sentiment #strict: Learning Higher-Level Features with Convolutional Restricted Boltzmann Machines for Sentiment Analysis (TH, YH, SMR), pp. 447–452.
ECIR-2015-NicosiaBM #rank: Learning to Rank Aggregated Answers for Crossword Puzzles (MN, GB, AM), pp. 556–561.
ECIR-2015-PasinatoMZ #elicitation #rating: Active Learning Applied to Rating Elicitation for Incentive Purposes (MBP, CEM, GZ), pp. 291–302.
ECIR-2015-PelejaM #retrieval #sentiment: Learning Sentiment Based Ranked-Lexicons for Opinion Retrieval (FP, JM), pp. 435–440.
ICML-2015-AmidU #multi: Multiview Triplet Embedding: Learning Attributes in Multiple Maps (EA, AU), pp. 1472–1480.
ICML-2015-BachHBG #performance: Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs (SHB, BH, JLBG, LG), pp. 381–390.
ICML-2015-Bou-AmmarTE #policy #sublinear: Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret (HBA, RT, EE), pp. 2361–2369.
ICML-2015-ChangKADL #education: Learning to Search Better than Your Teacher (KWC, AK, AA, HDI, JL), pp. 2058–2066.
ICML-2015-ChenSYU #modelling: Learning Deep Structured Models (LCC, AGS, ALY, RU), pp. 1785–1794.
ICML-2015-CilibertoMPR #multi: Convex Learning of Multiple Tasks and their Structure (CC, YM,