Update README.md

README.md

@@ -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*.