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Brain-Like Approach...
Brain-Like Approaches to Unsupervised Learning of Hidden Representations - A Comparative Study
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- Ravichandran, Naresh Balaji (author)
- KTH,Beräkningsvetenskap och beräkningsteknik (CST)
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- Lansner, Anders, Professor (author)
- KTH,Beräkningsvetenskap och beräkningsteknik (CST)
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- Herman, Pawel, 1979- (author)
- KTH,Beräkningsvetenskap och beräkningsteknik (CST)
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(creator_code:org_t)
- 2021-09-07
- 2021
- English.
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In: Artificial Neural Networks And Machine Learning, ICANN 2021, Pt V. - Cham : Springer Nature. ; , s. 162-173
- Related links:
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http://arxiv.org/pdf...
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Neural networks
- Bio-inspired
- Hebbian learning
- Unsupervised learning
- Structural plasticity
Publication and Content Type
- ref (subject category)
- kon (subject category)
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