Technical Program

Paper Detail

Paper:MLSP-L3.1
Session:Learning Theory and Modeling
Time:Friday, May 21, 15:30 - 15:50
Presentation: Lecture
Topic: Machine Learning for Signal Processing: Learning Theory and Modeling
Title: THEORY OF MONTE CARLO SAMPLING-BASED ALOPEX ALGORITHMS FOR NEURAL NETWORKS
Authors: Zhe Chen; McMaster University 
 Simon Haykin; McMaster University 
 Suzanna Becker; McMaster University 
Abstract: We propose two novel Monte Carlo sampling-based Alopex algorithmsfor training neural networks. The proposed algorithms naturallycombine the sequential Monte Carlo estimation and Alopex-likeprocedure for gradient-free optimization, and the learningproceeds within the recursive Bayesian estimation framework.Experimental results on various problems show encouragingconvergence results.
 
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