MLSP-10.4
DICTIONARY LEARNING WITH UNIFORM SPARSE REPRESENTATIONS FOR ANOMALY DETECTION
Paul Irofti, Cristian Rusu, Andrei Patrascu, University of Bucharest, Faculty of Mathematics and Computer Science, Research Center for Logic, Optimization and Security (LOS), Romania
Session:
Sparsity Aware Processing
Track:
Machine Learning for Signal Processing
Location:
Gather Area F
Presentation Time:
Sun, 8 May, 23:00 - 23:45 China Time (UTC +8)
Sun, 8 May, 15:00 - 15:45 UTC
Sun, 8 May, 15:00 - 15:45 UTC
Session Chair:
Anand Sarwate, Rutgers University
Session MLSP-10
MLSP-10.1: LEARNING TO SAMPLE FOR SPARSE SIGNALS
Satish Mulleti, Haiyang Zhang, Weizmann Institute of Science, Rehovot, Israel, Israel; Yonina C. Eldar, Weizmann Institute of Science, Israel
MLSP-10.2: MIXTURE MODEL AUTO-ENCODERS: DEEP CLUSTERING THROUGH DICTIONARY LEARNING
Alexander Lin, Demba Ba, Harvard University, United States of America; Andrew Song, Massachusetts Institute of Technology, United States of America
MLSP-10.3: EXPLORING THE EFFECT OF L0/L2 REGULARIZATION IN NEURAL NETWORK PRUNING USING THE LC TOOLKIT
Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán, UC Merced, United States of America
MLSP-10.4: DICTIONARY LEARNING WITH UNIFORM SPARSE REPRESENTATIONS FOR ANOMALY DETECTION
Paul Irofti, Cristian Rusu, Andrei Patrascu, University of Bucharest, Faculty of Mathematics and Computer Science, Research Center for Logic, Optimization and Security (LOS), Romania
MLSP-10.5: DATA-DRIVEN SPATIALLY DEPENDENT PDE IDENTIFICATION
Ruixian Liu, Michael Bianco, Peter Gerstoft, Bhaskar Rao, University of California, San Diego, United States of America
MLSP-10.6: SPARSITY IMPROVES UNSUPERVISED ATTRIBUTE DISCOVERY IN STYLEGAN
Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Lawrence Livermore National Laboratory, United States of America