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@ -18,16 +18,21 @@ This is an on-going attempt to consolidate all interesting efforts in the area o
* Beyond saliency: understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation [pdf](https://arxiv.org/abs/1712.08268)
* Explaining NonLinear Classification Decisions With Deep Tayor Decomposition [pdf](https://arxiv.org/abs/1512.02479)
## 4. Bayesian approaches
## 4. Inverting Neural Networks
* Understanding Deep Image Representations by Inverting Them [pdf](https://arxiv.org/abs/1412.0035)
* Inverting Visual Representations with Convolutional Networks [pdf](https://arxiv.org/abs/1506.02753)
* Neural network inversion beyond gradient descent [pdf](http://opt-ml.org/papers/OPT2017_paper_38.pdf)
## 5. Bayesian approaches
* Yang, S. C. H., & Shafto, P. Explainable Artificial Intelligence via Bayesian Teaching. NIPS 2017 [pdf](http://shaftolab.com/assets/papers/yangShafto_NIPS_2017_machine_teaching.pdf)
## 5. Distilling DNNs into more interpretable models
## 6. Distilling DNNs into more interpretable models
* Interpreting CNNs via Decision Trees [pdf](https://arxiv.org/abs/1802.00121)
* Distilling a Neural Network Into a Soft Decision Tree [pdf](https://arxiv.org/abs/1711.09784)
## 6. DNNs that learn to explain
## 7. DNNs that learn to explain
* Deep Learning for Case-Based Reasoning through Prototypes [pdf](https://arxiv.org/pdf/1710.04806.pdf)
## 7. Others
## 8. Others
* Understanding Deep Architectures by Interpretable Visual Summaries [pdf](https://arxiv.org/pdf/1801.09103.pdf)