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@ -87,7 +87,7 @@ This is an on-going attempt to consolidate interesting efforts in the area of un
* Integrated Gradients: Axiomatic Attribution for Deep Networks. _Sundararajan et al. 2018_ [pdf](http://proceedings.mlr.press/v70/sundararajan17a/sundararajan17a.pdf) | [code](https://github.com/ankurtaly/Integrated-Gradients)
* The (Un)reliability of saliency methods. _Kindermans et al. 2018_ [pdf](https://openreview.net/forum?id=r1Oen--RW)
* Sanity Checks for Saliency Maps. _Adebayo et al. 2018_ [pdf](http://papers.nips.cc/paper/8160-sanity-checks-for-saliency-maps.pdf)
* Visual explanation by interpretation: Improving visual feedback capabilities of deep neural networks. _Oramas et al. 2019_ [pdf](https://arxiv.org/pdf/1712.06302.pdf)
#### Black-box
* RISE: Randomized Input Sampling for Explanation of Black-box Models. _Petsiuk et al. 2018_ [pdf](https://arxiv.org/pdf/1806.07421.pdf)