Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond

2022/09/14 by Xuhong Li, Haoyi Xiong, Xingjian Li +5 · 1 voice · 6 citations
Computer Science · #Adversarial Robustness in Machine Learning #Explainable Artificial Intelligence (XAI) #Topic Modeling

paper · doi:10.1007/s10115-022-01756-8

crossref issued 2022/09/14 · crossref published 2022/09/14 · crossref published-online 2022/09/14 · openalex publication_date 2022/09/14 · crossref created 2022/09/14 · crossref deposited 2022/10/21 · crossref published-print 2022/12/01 · openalex created_date 2025/10/10 · crossref indexed 2026/07/28 · openalex updated_date 2026/07/29

Citations

• Understanding deep learning (still) requires rethinking generalization
• Methods for Interpreting and Understanding Deep Neural Networks
• A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
• Learning dexterous in-hand manipulation
• Shortcut learning in deep neural networks
• Deep Reinforcement Learning: A Brief Survey
• Trustworthy AI: From Principles to Practices
• Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

Cited by

• Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities
• An Interpretable Deep Learning Model for General Insurance Pricing
• Attribution Explanations for Deep Neural Networks: A Theoretical Perspective
• From Prediction to Explanation: Multimodal, Explainable, and Interactive Deepfake Detection Framework for Non-Expert Users
• Unlocking Interpretability for RF Sensing: A Complex-Valued White-Box Transformer
• PixelNav: Towards Model-based Vision-Only Navigation with Topological Graphs

Discussions

• Glad that I found some recent literature that shares my concerns, and I would want this to be part of the modelling effort. DNA/Protein/Genomic Language modelling bros for "designing new synthetic lif [bsky, 2 points, 0 comments]