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WE3.I: Machine Learning Applications II

Session Type: Poster
Time: Wednesday, 6 May, 16:30 - 18:30
Location: On-Demand
Session Chair: Wee Peng Tay, Nanyang Technological University
 
   WE3.I.1: BLIND BOUNDED SOURCE SEPARATION USING NEURAL NETWORKS WITH LOCAL LEARNING RULES
         Alper Erdogan; Koc University
         Cengiz Pehlevan; Harvard University
 
   WE3.I.2: MODELING PIECE-WISE STATIONARY TIME SERIES
         Daoping Wu; Purdue University
         Suhas Gundimeda; Purdue University
         Shaoshuai Mou; Purdue University
         Christopher Quinn; Purdue University
 
   WE3.I.3: MULTIVARIATE TROPICAL REGRESSION AND PIECEWISE-LINEAR SURFACE FITTING
         Petros Maragos; National Technical University of Athens
         Emmanouil Theodosis; Harvard University
 
   WE3.I.4: REVEALING BACKDOORS, POST-TRAINING, IN DNN CLASSIFIERS VIA NOVEL INFERENCE ON OPTIMIZED PERTURBATIONS INDUCING GROUP MISCLASSIFICATION
         Zhen Xiang; Pennsylvania State University
         David Miller; Anomalee, Inc.
         George Kesidis; Pennsylvania State University
 
   WE3.I.5: CLASSIFYING PARTIALLY LABELED NETWORKED DATA VIA LOGISTIC NETWORK LASSO
         Nguyen Tran; Aalto University
         Henrik Ambos; Aalto University
         Alexander Jung; Aalto University
 
   WE3.I.6: NEURAL TIME WARPING FOR MULTIPLE SEQUENCE ALIGNMENT
         Keisuke Kawano; Toyota Central R&D Labs. Inc.
         Takuro Kutsuna; Toyota Central R&D Labs. Inc.
         Satoshi Koide; Toyota Central R&D Labs. Inc.
 
   WE3.I.7: LANCE: EFFICIENT LOW-PRECISION QUANTIZED WINOGRAD CONVOLUTION FOR NEURAL NETWORKS BASED ON GRAPHICS PROCESSING UNITS
         Guangli Li; Institute of Computing Technology, Chinese Academy of Sciences
         Lei Liu; Institute of Computing Technology, Chinese Academy of Sciences
         Xueying Wang; Institute of Computing Technology, Chinese Academy of Sciences
         Xiu Ma; Jilin University
         Xiaobing Feng; Institute of Computing Technology, Chinese Academy of Sciences
 
   WE3.I.8: MEDIA CLASSIFICATION WITH BAYESIAN OPTIMIZATION AND VAPNIK-CHERVONENKIS (VC) BOUNDS
         Sunil Bharitkar; HP Inc.
 
   WE3.I.9: BATMAN: BAYESIAN TARGET MODELLING FOR ACTIVE INFERENCE
         Magnus T. Koudahl; Eindhoven University of Technology
         Bert de Vries; Eindhoven University of Technology and GN Hearing
 
   WE3.I.10: DEEP LEARNING ABILITIES TO CLASSIFY INTRICATE VARIATIONS IN TEMPORAL DYNAMICS OF MULTIVARIATE TIME SERIES
         Pierre Liotet; Ecole normale supérieure Paris-Saclay
         Patrice Abry; Ecole Normale Superieure de Lyon
         Roberto Leonarduzzi; Ecole Normale Supérieure
         Marc Senneret; Vivienne Investissement
         Laurent Jaffrès; Vivienne Investissement
         Geral Perrin; Vivienne Investissement
 
   WE3.I.11: ASSIMILATION-BASED LEARNING OF CHAOTIC DYNAMICAL SYSTEMS FROM NOISY AND PARTIAL DATA
         Duong Nguyen; IMT Atlantique, Lab-STICC
         Said Ouala; IMT Atlantique, Lab-STICC
         Lucas Drumetz; IMT Atlantique, Lab-STICC
         Ronan Fablet; IMT Atlantique, Lab-STICC
 
   WE3.I.12: GATED MULTI-LAYER CONVOLUTIONAL FEATURE EXTRACTION NETWORK FOR ROBUST PEDESTRIAN DETECTION
         Tianrui Liu; Imperial College London
         Jun-Jie Huang; Imperial College London
         Tianhong Dai; Imperial College London
         Guangyu Ren; Imperial College London
         Tania Stathaki; Imperial College London