MLSP-4.2
DEEP PIECEWISE HASHING FOR EFFICIENT HAMMING SPACE RETRIEVAL
Jingzi Gu, Dayan Wu, Peng Fu, Bo Li, Weiping Wang, Institute of Information Engineering, Chinese Academy of Sciences, China
Session:
Deep Learning I
Track:
Machine Learning for Signal Processing
Location:
Gather Area F
Presentation Time:
Sun, 8 May, 21:00 - 21:45 China Time (UTC +8)
Sun, 8 May, 13:00 - 13:45 UTC
Sun, 8 May, 13:00 - 13:45 UTC
Session Chair:
Sijia Liu, Michigan State University
Session MLSP-4
MLSP-4.1: UNCERTAINTY IN DATA-DRIVEN KALMAN FILTERING FOR PARTIALLY KNOWN STATE-SPACE MODELS
Itzik Klein, University of Haifa, Israel; Guy Revach, Jonas E. Mehr, ETH Zürich, Switzerland; Nir Shlezinger, Ben-Gurion University of the Negev, Israel; Ruud J. G. van Sloun, Eindhoven University of Technology, and with Phillips Research, Netherlands; Yonina. C. Eldar, Weizmann Institute of Science, Israel
MLSP-4.2: DEEP PIECEWISE HASHING FOR EFFICIENT HAMMING SPACE RETRIEVAL
Jingzi Gu, Dayan Wu, Peng Fu, Bo Li, Weiping Wang, Institute of Information Engineering, Chinese Academy of Sciences, China
MLSP-4.3: SODA: Self-organizing data augmentation in deep neural networks - Application to biomedical image segmentation tasks
Arnaud Deleruyelle, John Klein, Cristian Versari, Université de Lille, France
MLSP-4.4: DEEP IMPULSE RESPONSES: ESTIMATING AND PARAMETERIZING FILTERS WITH DEEP NETWORKS
Alexander Richard, Peter Dodds, Vamsi Krishna Ithapu, Facebook Inc, United States of America
MLSP-4.5: JOINT TEMPORAL CONVOLUTIONAL NETWORKS AND ADVERSARIAL DISCRIMINATIVE DOMAIN ADAPTATION FOR EEG-BASED CROSS-SUBJECT EMOTION RECOGNITION
Zhipeng He, Yongshi Zhong, Jiahui Pan, South China Normal University, China
MLSP-4.6: GRADIENT VARIANCE LOSS FOR STRUCTURE-ENHANCED IMAGE SUPER-RESOLUTION
Lusine Abrahamyan, Nikos Deligiannis, Vrije Universiteit Brussel, Belgium; Anh Minh Truong, Wilfried Philips, Ghent University, Belgium