Visualizing Information Bottleneck through Variational Inference
| Autor: | Herwana, Cipta, Kadian, Abhishek |
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| Rok vydání: | 2022 |
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| Druh dokumentu: | Working Paper |
| Popis: | The Information Bottleneck theory provides a theoretical and computational framework for finding approximate minimum sufficient statistics. Analysis of the Stochastic Gradient Descent (SGD) training of a neural network on a toy problem has shown the existence of two phases, fitting and compression. In this work, we analyze the SGD training process of a Deep Neural Network on MNIST classification and confirm the existence of two phases of SGD training. We also propose a setup for estimating the mutual information for a Deep Neural Network through Variational Inference. Comment: arXiv admin note: text overlap with arXiv:1703.00810, arXiv:2202.06749 by other authors |
| Databáze: | arXiv |
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