Name
Title | ||
|---|---|---|
Brain-Like Approaches to Unsupervised Learning of Hidden Representations - A Comparative Study |
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-86383-8_13 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2021, PT V |
Keywords | DocType | Volume |
Neural networks, Bio-inspired, Hebbian learning, Unsupervised learning, Structural plasticity | Conference | 12895 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ravichandran Naresh Balaji | 1 | 0 | 0.34 |
| Anders Lansner | 2 | 647 | 100.03 |
| Pawel Herman | 3 | 8 | 1.50 |