The Analysis of Deep Neural Networks by Information Theory: From Explainability to Generalization
Keywords:New Faculty Highlights
AbstractDespite their great success in many artificial intelligence tasks, deep neural networks (DNNs) still suffer from a few limitations, such as poor generalization behavior for out-of-distribution (OOD) data and the "black-box" nature. Information theory offers fresh insights to solve these challenges. In this short paper, we briefly review the recent developments in this area, and highlight our contributions.
How to Cite
Yu, S. (2023). The Analysis of Deep Neural Networks by Information Theory: From Explainability to Generalization. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 15462-15462. https://doi.org/10.1609/aaai.v37i13.26829
New Faculty Highlights