The ideal data compression and automatic discovery of hidden law using neural network

• URL: http://arxiv.org/abs/2203.16941v1
• Date: Thu, 31 Mar 2022 10:55:24 GMT
• Title: The ideal data compression and automatic discovery of hidden law using neural network
• Authors: Taisuke Katayose
• Abstract summary: We consider how the human brain recognizes events and memorizes them. We reproduce the system of the human brain on a machine learning model with a new autoencoder neural network.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently machine learning using neural networks has been developed, and many new methods have been suggested. On the other hand, a system that has true versatility has not been developed, and there remain many fields in which the human brain has advantages over machine learning. We considered how the human brain recognizes events and memorizes them and succeeded to reproduce the system of the human brain on a machine learning model with a new autoencoder neural network (NN). The previous autoencoders have the problem that they cannot define well what is the features of the input data, and we need to restrict the middle layer of the autoencoder artificially. We solve this problem by defining a new loss function that reflects the information entropy, and it enables the NN to compress the input data ideally and automatically discover the hidden law behind the input data set. The loss function used in our NN is based on the free-energy principle which is known as the unified brain theory, and our study is the first concrete formularization of this principle. The result of this study can be applied to any kind of data analysis and also to cognitive science.

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