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Avishek Anand
interpretability
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Avishek Anand
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@@ -35,3 +35,27 @@ We release [InterpretMe]
1.
**Right for the right reasons: Training differentiable models by constraining their explanations.**
*Andrew Slavin Ross, Michael C. Hughes, and Finale Doshi-Velez.*
.IJCAI 2018.
[
paper
](
https://doi.org/10.24963/ijcai.2017/371
)
1.
**Sharing Deep Neural Network Models with Interpretation.**
*Huijun Wu, Chen Wang, Jie Yin, Kai Lu and Liming Zhu*
. WWW’18.
[
paper
](
https://doi.org/10.24963/ijcai.2017/371
)
1.
**TEM:Tree-enhanced Embedding Model for Explainable Recommendation Xiang Wang.**
*Xiangnan He, Fuli Feng, Liqiang Nie and Tat-Seng Chua*
. WWW’18. [paper]
1.
**Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music.**
*Haizi Yu, Lav R. Varshney*
. ICLR’17. [paper]
1.
**Generating Interpretable Images with Controllable Structure**
*Scott Reed, Aron van den Oord, Nal Kalchbrenner, Victor Bapst, Matt Botvinick, Nando de Freitas*
. ICLR’17. [paper]
1.
**Supervised topic models for clinical interpretability.**
*Hughes et al.*
. 2016.
1.
**An Effective and Interpretable Method for Document Classification**
*Ngo Van Linh, Nguyen Kim Anh, Khoat Than, Chien Nguyen Dang*
.
1.
**Interpretable probabilistic embeddings: bridging the gap between topic models and neural networks**
*Anna Potapenko, Artem Popov, and Konstantin Vorontsov*
.
1.
**Interpretable Explanations of Black Boxes by Meaningful Perturbation.**
*Fong, Ruth C and Vedaldi, Andrea*
.
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