Brain-like approaches to unsupervised learning of hidden representations -- a comparative study
Neural and Evolutionary Computing
2021-04-19 v2 Machine Learning
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.
Cite
@article{arxiv.2005.03476,
title = {Brain-like approaches to unsupervised learning of hidden representations -- a comparative study},
author = {Naresh Balaji Ravichandran and Anders Lansner and Pawel Herman},
journal= {arXiv preprint arXiv:2005.03476},
year = {2021}
}
Comments
arXiv admin note: text overlap with arXiv:2003.12415