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.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Interpretability
Machine Learning > Optimization & Theory > Learning Theory
Deep Learning > Architectures > Neural Networks
Machine Learning > Optimization & Theory > Information Theory
Machine Learning > Learning Types > Representation Learning
Deep Learning > Optimization & Theory > Theory
Machine Learning > Core Methods > Interpretability