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

Naresh Balaji Ravichandran; Anders Lansner; Pawel Herman

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arXiv · 2005.03476

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

Naresh Balaji Ravichandran·Anders Lansner·Pawel Herman

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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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Naresh Balaji Ravichandran, Anders Lansner, Pawel Herman. 2020-05-06. Brain-like approaches to unsupervised learning of hidden representations -- a comparative study. https://arxiv.org/abs/2005.03476

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