SPTM-L4: Bayesian Signal Processing
Wed, 17 Apr, 16:30 - 18:30 (UTC +9)
Location: Room 201
Session Type: Lecture
Session Co-Chairs: Petar Djuric, Stony Brook University and Nir Shlezinger, Ben-Gurion University
Track: Signal Processing Theory and Methods
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Wed, 17 Apr, 16:30 - 16:50 (UTC +9)
 

SPTM-L4.1: UNITARY APPROXIMATE MESSAGE PASSING FOR MATRIX FACTORIZATION
Zhengdao Yuan, Open University of Henan, China; Qinghua Guo, University of Wollongong, Australia; Yonina C. Eldar, Weizmann Institute of Science, Israel; Yonghui Li, University of Sydney, Australia
Wed, 17 Apr, 16:50 - 17:10 (UTC +9)
 

SPTM-L4.2: LEARN TO TRACK-BEFORE-DETECT VIA NEURAL DYNAMIC PROGRAMMING
Eyal Fishel, Nikita Tsarov, Tslil Tapiro, Itay Nuri, Nir Shlezinger, Ben-Gurion University, Israel
Wed, 17 Apr, 17:10 - 17:30 (UTC +9)
 

SPTM-L4.3: VECTOR APPROXIMATE MESSAGE PASSING WITH ARBITRARY I.I.D. NOISE PRIORS
Mohamed Akrout, Tiancheng Gao, Faouzi Bellili, Amine Mezghani, University of Manitoba, Canada
Wed, 17 Apr, 17:30 - 17:50 (UTC +9)
 

SPTM-L4.4: DISTRIBUTED VECTOR APPROXIMATE MESSAGE PASSING
Mukilan Karuppasamy, Mohamed Akrout, Faouzi Bellili, Amine Mezghani, University of Manitoba, Canada
Wed, 17 Apr, 17:50 - 18:10 (UTC +9)
 

SPTM-L4.5: END-TO-END LEARNING OF GAUSSIAN MIXTURE PROPOSALS USING DIFFERENTIABLE PARTICLE FILTERS AND NEURAL NETWORKS
Benjamin Cox, University of Edinburgh, United Kingdom of Great Britain and Northern Ireland; Sara Perez-Vieites, IMT Nord Europe, France; Nicolas Zilberstein, Martin Sevilla, Santiago Segarra, Rice University, United States of America; Víctor Elvira, University of Edinburgh, United Kingdom of Great Britain and Northern Ireland
Wed, 17 Apr, 18:10 - 18:30 (UTC +9)
 

SPTM-L4.6: Dynamic random feature Gaussian Processes for Bayesian optimization of time-varying functions
Fernando Llorente, Petar Djuric, Stony Brook University, United States of America