Sökning: id:"swepub:oai:DiVA.org:kth-305420" >
Brain-Like Approach...
Brain-Like Approaches to Unsupervised Learning of Hidden Representations - A Comparative Study
-
- Ravichandran, Naresh Balaji (författare)
- KTH,Beräkningsvetenskap och beräkningsteknik (CST)
-
- Lansner, Anders, Professor (författare)
- KTH,Beräkningsvetenskap och beräkningsteknik (CST)
-
- Herman, Pawel, 1979- (författare)
- KTH,Beräkningsvetenskap och beräkningsteknik (CST)
-
(creator_code:org_t)
- 2021-09-07
- 2021
- Engelska.
-
Ingår i: Artificial Neural Networks And Machine Learning, ICANN 2021, Pt V. - Cham : Springer Nature. ; , s. 162-173
- Relaterad länk:
-
http://arxiv.org/pdf...
-
visa fler...
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Neural networks
- Bio-inspired
- Hebbian learning
- Unsupervised learning
- Structural plasticity
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)