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@ -31,7 +31,7 @@ This is an on-going attempt to consolidate all interesting efforts in the area o
# Heatmaps / Attribution
* Learning how to explain neural networks: PatternNet and PatternAttribution [pdf](https://arxiv.org/abs/1705.05598)
* A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations [pdf](https://arxiv.org/abs/1805.07039)
* A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations. _Nie et al. 2018_ [pdf](https://arxiv.org/abs/1805.07039)
* A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks [pdf](https://arxiv.org/pdf/1606.07757.pdf)
* How Important Is a Neuron? _Dhamdhere et al._ 2018 [pdf](https://arxiv.org/pdf/1805.12233.pdf)
@ -57,4 +57,4 @@ This is an on-going attempt to consolidate all interesting efforts in the area o
# Understanding via Mathematical and Statistical tools
* Understanding Deep Architectures by Interpretable Visual Summaries [pdf](https://arxiv.org/pdf/1801.09103.pdf)
* A Peek Into the Hidden Layers of a Convolutional Neural Network Through a Factorization Lens [pdf](https://arxiv.org/abs/1806.02012)
* A Peek Into the Hidden Layers of a Convolutional Neural Network Through a Factorization Lens. _Saini et al. 2018_ [pdf](https://arxiv.org/abs/1806.02012)