Brain-Like Approaches to Unsupervised Learning of Hidden Representations - A Comparative Study

Ravichandran, Naresh Balaji (author): KTH,Beräkningsvetenskap och beräkningsteknik (CST)

Lansner, Anders, Professor (author): KTH,Beräkningsvetenskap och beräkningsteknik (CST)

Herman, Pawel, 1979- (author): KTH,Beräkningsvetenskap och beräkningsteknik (CST)

(creator_code:org_t)

2021-09-07
2021
English.
In: Artificial Neural Networks And Machine Learning, ICANN 2021, Pt V. - Cham : Springer Nature. ; , s. 162-173

Related links:: http://arxiv.org/pdf...; show more...; https://urn.kb.se/re...; https://doi.org/10.1...; show less...

Abstract Subject headings

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.

By the author/editor: Ravichandran, Na ...; Lansner, Anders, ...; Herman, Pawel, 1 ...

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Computer Science ...

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