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About Opening The Black Box of Deep Neural Networks Via Information
This document analyzes deep neural networks using an information theory approach. It finds that training has two phases - an initial error minimization phase followed by a longer representation compression phase where layers reduce information about the input. Layers converge close to the information bottleneck bound and satisfy self-consistent encoder-decoder relationships. Hidden layers speed up training by enabling faster stochastic relaxation during compression.
Detailed Information
| Author: | ['Farid Rasoolzadeh Baghmisheh'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 19 |
| Language: | English |
| Format: | |
| Price: | FREE |
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