Theoretical interpretation of neural network learning

Abstract

In recent years, the topic of AI has been rising every day. In particular, there are many topics related to neural networks, including generative AI. However, some say that while the results produced by neural networks are good, the basis for this is unclear. Additionally, deep learning has been successful with a number of different models. For example, CNN can already identify objects with a precision that exceeds human recognition. In LLM as well, models that follow the flow of Transformer have achieved remarkable results. However, as with neural networks in general, there are few theoretical research results that explain why these results are obtained. Therefore, this research focuses on the learning of neural networks, and aims to help the theoretical understanding of neural networks by explaining the learning results mathematically from the characteristics of learning and the model being learned.