SS-L11.3
LARGE-SYSTEM FIXED-POINT LAW AND DETERMINISTIC CLOSURE FOR SPARSE BAYESIAN LEARNING
Fangqing Xiao, Dirk Slock, Eurecom, France
Session:
SS-L11: Large-Dimensional Structures and Methods for Signal Processing, Communication, and Machine Learning Oral
Track:
Special Sessions
Location:
Room 116
Presentation Time:
Thu, 7 May, 14:40 - 15:00
Presentation
Discussion
Resources
No resources available.
Session SS-L11
SS-L11.1: INCORPORATING PRIORS IN LEARNING: A RANDOM MATRIX STUDY UNDER A TEACHER–STUDENT FRAMEWORK
MALIK TIOMOKO, Huawei Technologies, France; Ekkehard Schnoor, Aalto University, Finland
SS-L11.2: ORTHOGONAL APPROXIMATE MESSAGE-PASSING FOR SUBLINEAR SPARSITY
Keigo Takeuchi, Toyohashi University of Technology, Japan
SS-L11.3: LARGE-SYSTEM FIXED-POINT LAW AND DETERMINISTIC CLOSURE FOR SPARSE BAYESIAN LEARNING
Fangqing Xiao, Dirk Slock, Eurecom, France
SS-L11.4: AN IMPROVED CONVERGENCE ANALYSIS OF GOSSIP METHODS FOR LARGE RANDOM GRAPHS
Yue Xu, Zhenyu Liao, Huazhong University of Science and Technology, China
SS-L11.5: ORTHOGONAL APPROXIMATE MESSAGE PASSING ALGORITHMS FOR RECTANGULAR SPIKED MATRIX MODELS WITH ROTATIONALLY INVARIANT NOISE
Haohua Chen, Academy of Mathematics and Systems Science, China; Songbin Liu, Columbia University, United States of America; Junjie Ma, Academy of Mathematics and Systems Science, China
SS-L11.6: ONE-BIT QUANTIZED PRECODER CHARACTERIZATION AND PARAMETER OPTIMIZATION IN MASSIVE MIMO SYSTEMS
Xiuxiu Ma, Abla Kammoun, Tareq Y. Al-Naffouri, King Abdullah University of Science and Technology, Saudi Arabia
Contacts