Brain-like approaches to unsupervised learning of hidden representations -- a comparative study

Unsupervised learning of hidden representations has been one of the most vibrant research directions in machine learning in recent years. In this work we study the brain-like Bayesian Confidence Propagating Neural Network (BCPNN) model, recently extended to extract sparse distributed high-dimensiona...

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Bibliographic Details
Published in:arXiv.org 2021-04
Main Authors: Naresh Balaji Ravichandran, Lansner, Anders, Herman, Pawel
Format: Article
Language:English
Subjects:
Brain
Comparative studies
Machine learning
Neural networks
Representations
Unsupervised learning
Online Access:Get full text
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Summary:Unsupervised learning of hidden representations has been one of the most vibrant research directions in machine learning in recent years. In this work we study the brain-like Bayesian Confidence Propagating Neural Network (BCPNN) model, recently extended to extract sparse distributed high-dimensional representations. The usefulness and class-dependent separability of the hidden representations when trained on MNIST and Fashion-MNIST datasets is studied using an external linear classifier and compared with other unsupervised learning methods that include restricted Boltzmann machines and autoencoders.
ISSN:2331-8422