SS-L21.5
BAYESIAN LEARNING-BASED KALMAN SMOOTHING FOR LINEAR DYNAMICAL SYSTEMS WITH UNKNOWN SPARSE INPUTS
Rupam Kalyan Chakraborty, Indian Institute of Science, India; Geethu Joseph, Delft University of Technology, Netherlands; Chandra R. Murthy, Indian Institute of Science, India
Session:
SS-L21: Variational Inference and Approximate Bayesian Techniques Lecture
Track:
Special Sessions
Location:
Room E8
Presentation Time:
Fri, 19 Apr, 09:40 - 10:00 (UTC +9)
Session Co-Chairs:
Dirk Slock, EURECOM and Sergios Theodoridis, Aalborg University
Session SS-L21
SS-L21.1: DECENTRALIZED GENERALIZED APPROXIMATE MESSAGE-PASSING FOR TREE-STRUCTURED NETWORKS
Keigo Takeuchi, Toyohashi University of Technology, Japan
SS-L21.2: Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models
Zhidi Lin, The Chinese University of Hong Kong, Shenzhen, China; Juan Maroñas, Autonomous University of Madrid, and Cognizant, Spain; Ying Li, The University of Hong Kong, China; Feng Yin, The Chinese University of Hong Kong, Shenzhen, China; Sergios Theodoridis, National and Kapodistrian University of Athens, Greece
SS-L21.3: INFERENCE OF GENETIC EFFECTS VIA APPROXIMATE MESSAGE PASSING
Al Depope, Marco Mondelli, Matthew Robinson, Institute of Science and Technology Austria (ISTA), Austria
SS-L21.4: VECTOR APPROXIMATE MESSAGE PASSING FOR NOT SO LARGE N.I.I.D. GENERALIZED I/O LINEAR MODELS
Zilu Zhao, Fangqing Xiao, Dirk Slock, Eurecom, France
SS-L21.5: BAYESIAN LEARNING-BASED KALMAN SMOOTHING FOR LINEAR DYNAMICAL SYSTEMS WITH UNKNOWN SPARSE INPUTS
Rupam Kalyan Chakraborty, Indian Institute of Science, India; Geethu Joseph, Delft University of Technology, Netherlands; Chandra R. Murthy, Indian Institute of Science, India
SS-L21.6: ESTIMATION OF SPECTRAL LINES USING EXPECTATION PROPAGATION
Jiang Zhu, Xupeng Lei, Zhejiang University, China; Mihai-Alin Badiu, University of Oxford, United Kingdom of Great Britain and Northern Ireland
Contacts