Commit e1b63fc4 authored by Avishek Anand's avatar Avishek Anand

Update README.md

parent 9f060af8
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1. **Towards a rigorous science of interpretable machine learning.**
*Finale Doshi-Velez and Been Kim.* 2017. [paper](https://arxiv.org/pdf/1702.08608.pdf)
1. **Streaming weak submodularity: Interpreting neural networks on the fly.**
*Ethan R Elenberg, Alexandros G Dimakis, Moran Feldman, and Amin Karbasi*. 2017 [paper](https://arxiv.org/pdf/1703.02647).
1. **Interpretable explanations of black boxes by meaningful perturbation.**
*Ruth C Fong and Andrea Vedaldi.*.CVPR 2017. [paper](https://arxiv.org/pdf/1704.03296.pdf)
1. **Supervised topic models for clinical interpretability.**
*Michael C Hughes, Huseyin Melih Elibol, Thomas McCoy, Roy Perlis, and Finale Doshi-Velez*.2016. [paper](https://arxiv.org/pdf/1612.01678)
1. **A unified approach to interpreting model predictions.**
*Scott Lundberg and Su-In Lee*.2016. [paper](https://arxiv.org/pdf/1705.07874)
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1. **TEM:Tree-enhanced Embedding Model for Explainable Recommendation Xiang Wang.**
*Xiangnan He, Fuli Feng, Liqiang Nie and Tat-Seng Chua*. WWW’18. [paper](https://www.comp.nus.edu.sg/~xiangnan/papers/www18-tem.pdf)
1. **Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music.**
*Haizi Yu, Lav R. Varshney*. ICLR’17. [paper](https://openreview.net/pdf?id=ryhqQFKgl)
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](http://www.scottreed.info/files/iclr2017.pdf)
1. **Supervised topic models for clinical interpretability.**
*Hughes et al.*. 2016.[paper](https://arxiv.org/pdf/1612.01678.pdf)
1. **An Effective and Interpretable Method for Document Classification**
*Ngo Van Linh, Nguyen Kim Anh, Khoat Than, Chien Nguyen Dang*. KAIS 2016.[paper](http://is.hust.edu.vn/~khoattq/papers/kais-2016.pdf)
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