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

• URL: http://arxiv.org/abs/2005.03476v2
• Date: Fri, 16 Apr 2021 13:22:54 GMT
• Title: Brain-like approaches to unsupervised learning of hidden representations -- a comparative study
• Authors: Naresh Balaji Ravichandran, Anders Lansner, Pawel Herman
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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