2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-15.3
Paper Title Information and Regularization in Restricted Boltzmann Machines
Authors Matias Vera, CONICET, Argentina; Leonardo Rey Vega, Universidad de Buenos Aires and CONICET, Argentina; Pablo Piantanida, Université Paris-Saclay, France
SessionMLSP-15: Learning Algorithms 2
LocationGather.Town
Session Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-INFO] Information-theoretic learning
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Recent works suggests an interesting interplay between the information flow between inputs features and hidden representations of a learning and the ability of the algorithm to generalize from trained samples to unobserved data. For instance, some the regularization techniques used to control generalization are expected to impact the corresponding information metrics. In this work, we study mutual information in Restricted Boltzmann Machines (RBM) and its relationship with the different regularization techniques. Our results show some evidence on interesting connections between the mutual information (inputs and its representations) with relevant parameters such as: network dimension, matrix norms and dropout probability, which are known to influence the generalization ability of the network. We empirically corroborates our results based on an numerical study.