XAI-papers/README.md

Papers on Explainable Artificial Intelligence

This is an on-going attempt to consolidate all interesting efforts in the area of understanding / interpreting / explaining / visualizing machine learning models.

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0. GUI tools

• Deep Visualization Toolbox code | pdf

1. Surveys

• Methods for Interpreting and Understanding Deep Neural Networks pdf
• The Mythos of Model Interpretability pdf
• Towards A Rigorous Science of Interpretable Machine Learning pdf
• Visualizations of Deep Neural Networks in Computer Vision: A Survey (Seifert et al. 2017) pdf
• How convolutional neural network see the world - A survey of convolutional neural network visualization methods (Qin et al. 2018) pdf
• A brief survey of visualization methods for deep learning models from the perspective of Explainable AI (Chalkiadakis 2018) pdf

2. Feature Visualization / Activation Maximization

• Visualizing higher-layer features of a deep network. Erhan et al. 2009 pdf
• Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. Nguyen et al. 2016 code | pdf
• Plug and Play Generative Networks Nguyen et al. 2017 pdf | code

3. Heatmap / Attribution

• Learning how to explain neural networks: PatternNet and PatternAttribution pdf
• A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations pdf
• A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks pdf

Layer-wise Backpropagation

• Beyond saliency: understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation pdf
• Explaining NonLinear Classification Decisions With Deep Tayor Decomposition pdf

4. Inverting Neural Networks

• Understanding Deep Image Representations by Inverting Them pdf
• Inverting Visual Representations with Convolutional Networks pdf
• Neural network inversion beyond gradient descent pdf

5. Bayesian approaches

• Yang, S. C. H., & Shafto, P. Explainable Artificial Intelligence via Bayesian Teaching. NIPS 2017 pdf

6. Distilling DNNs into more interpretable models

• Interpreting CNNs via Decision Trees pdf
• Distilling a Neural Network Into a Soft Decision Tree pdf

7. DNNs that learn to explain

• Deep Learning for Case-Based Reasoning through Prototypes pdf
• Unsupervised Learning of Neural Networks to Explain Neural Networks pdf

8. Others

• Understanding Deep Architectures by Interpretable Visual Summaries pdf