Correlation between entropy and generalizability in a neural network

• URL: http://arxiv.org/abs/2207.01996v1
• Date: Tue, 5 Jul 2022 12:28:13 GMT
• Title: Correlation between entropy and generalizability in a neural network
• Authors: Ge Zhang
• Abstract summary: We use Wang-Landau Mote Carlo algorithm to calculate the entropy at a given test accuracy. Our results show that entropical forces help generalizability.
• Score: 9.223853439465582
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although neural networks can solve very complex machine-learning problems, the theoretical reason for their generalizability is still not fully understood. Here we use Wang-Landau Mote Carlo algorithm to calculate the entropy (logarithm of the volume of a part of the parameter space) at a given test accuracy, and a given training loss function value or training accuracy. Our results show that entropical forces help generalizability. Although our study is on a very simple application of neural networks (a spiral dataset and a small, fully-connected neural network), our approach should be useful in explaining the generalizability of more complicated neural networks in future works.

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