SPTM-L3.5
BOATT: UNIFIED BAYESIAN ONLINE TRACKING AND ADAPTATION FOR DYNAMIC TENSOR STREAMS
Zikun Quan, University College London, United Kingdom of Great Britain and Northern Ireland; Weilin Zhou, Nanjing Tech University, China
Session:
SPTM-L3: Bayesian Signal Processing III Oral
Track:
Signal Processing Theory and Methods [TM]
Location:
Room 124+125
Presentation Time:
Wed, 6 May, 10:20 - 10:40
Session SPTM-L3
SPTM-L3.1: A SCALED POISSON BAYESIAN MODEL FOR VIRAL EPIDEMIC MONITORING
Barbara Pascal, Nantes Université, Ecole Centrale Nantes, CNRS, France; Patrice Abry, CNRS, ENS Lyon, France; Juliette Chevallier, INSA Toulouse, Université de Toulouse, France; Gersende Fort, CNRS, Université de Toulouse, France
SPTM-L3.2: Bayesian Uncertainty-Aware MRI Reconstruction
Ahmed Karam Eldaly, University of Exeter, United Kingdom of Great Britain and Northern Ireland; Matteo Figini, Daniel C. Alexander, University College London, United Kingdom of Great Britain and Northern Ireland
SPTM-L3.3: Recursive state estimation via approximate modal paths
Filip Tronarp, Lund University, Sweden
SPTM-L3.4: BAYESIAN SIGNAL SEPARATION VIA PLUG-AND-PLAY DIFFUSION-WITHIN-GIBBS SAMPLING
Yi Zhang, Rui Guo, Yonina Eldar, Weizmann Institute of Science, Israel
SPTM-L3.5: BOATT: UNIFIED BAYESIAN ONLINE TRACKING AND ADAPTATION FOR DYNAMIC TENSOR STREAMS
Zikun Quan, University College London, United Kingdom of Great Britain and Northern Ireland; Weilin Zhou, Nanjing Tech University, China
SPTM-L3.6: A NOVEL BAYESIAN EM-LIKE ALGORITHM FOR FAST COMPTON CAMERA IMAGING
Nhan Le, Hichem Snoussi, Troyes University of Technology, France
Contacts