2023
AAAI
AAAI 2023
The Analysis of Deep Neural Networks by Information Theory: From Explainability to Generalization
Abstract
Abstract Despite 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.
Authors
Topics
Artificial Intelligence > Core AI > Interpretability
Machine Learning > Core Methods > Representation Learning
Machine Learning > Optimization & Theory > Learning Theory
Deep Learning > Architectures > Neural Networks
Machine Learning > Optimization & Theory > Information Theory
Deep Learning > Optimization & Theory > Theory
Machine Learning > Core Methods > Interpretability