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January 8-10, 2020
Early Registration Deadline: December 15, 2019
Location: Himachal Pradesh, India
Website
Manuscript Due: December 15, 2019
Publication Date: September 2020
CFP Document
White Paper Due: October 21, 2019
Publication Date: November 2020
CFP Document
Lecture Date: September 26, 2019
Chapter: Taipei
Chapter Chair: Jing-Ming Guo
Topic: From Research to Products:
Empowering Retailing Experiences with Computer Vision
Lecture Date: November 4-5, 2019
Chapter: Israel
Chapter Chair: Arie Yeredor
Topic: Cyber Attacks on Internet of Things Sensor Systems for Inference
Lecture Date: September 5, 2019
Chapter: Chicago
Chapter Chair: Rashid Ansari
Topic: Speech Recognition: What’s Left?
Lecture Date: November 16, 2019
Chapter: Beijing
Chapter Chair: Qiuqi Ruan
Topic: Optimal Array Processing
Sequential Monte Carlo (SMC) methods comprise one of the most successful approaches to approximate Bayesian filtering. However, SMC without a good proposal distribution can perform poorly, in particular in high dimensions. We propose nested sequential Monte Carlo, a methodology that generalizes the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm.
The paper derives the stability bound of the initial mean-square deviation of an adaptive filtering algorithm based on minimizing the 2 L th moment of the estimation error, with L being an integer greater than 1. The analysis is done for a time-invariant plant with even input probability density function. Dependence of the stability bound on the algorithm step-size, type of the noise distribution, signal-to-noise ratio (SNR), and L is studied.