3.4 KiB
3.4 KiB
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.
0. GUI tools
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
- Synthesizing the preferred inputs for neurons in neural networks via deep generator networks code | pdf
- Plug and Play Generative Networks 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
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