SPTM-L4.4
DISTRIBUTED VECTOR APPROXIMATE MESSAGE PASSING
Mukilan Karuppasamy, Mohamed Akrout, Faouzi Bellili, Amine Mezghani, University of Manitoba, Canada
Session:
SPTM-L4: Bayesian Signal Processing Lecture
Track:
Signal Processing Theory and Methods
Location:
Room 201
Presentation Time:
Wed, 17 Apr, 17:30 - 17:50 (UTC +9)
Session Co-Chairs:
Petar Djuric, Stony Brook University and Nir Shlezinger, Ben-Gurion University
Session SPTM-L4
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
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
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
SPTM-L4.4: DISTRIBUTED VECTOR APPROXIMATE MESSAGE PASSING
Mukilan Karuppasamy, Mohamed Akrout, Faouzi Bellili, Amine Mezghani, University of Manitoba, Canada
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
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
Contacts