XAI/XAI-papers
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Definition papers

Browse Source
This commit is contained in:
Anh M. Nguyen 2019-05-21 12:07:54 -05:00 committed by GitHub
parent 5c5c413dbb
commit 14a764e95c
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 6 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
README.md
View File

@ -17,15 +17,18 @@ This is an on-going attempt to consolidate interesting efforts in the area of un
# Surveys # Surveys
* Methods for Interpreting and Understanding Deep Neural Networks. _Montavon et al. 2017_ [pdf](https://arxiv.org/pdf/1706.07979.pdf) * Methods for Interpreting and Understanding Deep Neural Networks. _Montavon et al. 2017_ [pdf](https://arxiv.org/pdf/1706.07979.pdf)
* The Mythos of Model Interpretability. _Lipton 2016_ [pdf](https://arxiv.org/abs/1606.03490)
* Towards A Rigorous Science of Interpretable Machine Learning. _Doshi-Velez & Kim. 2017_ [pdf](https://arxiv.org/pdf/1702.08608.pdf)
* Visualizations of Deep Neural Networks in Computer Vision: A Survey. _Seifert et al. 2017_ [pdf](https://link.springer.com/chapter/10.1007/978-3-319-54024-5_6) * Visualizations of Deep Neural Networks in Computer Vision: A Survey. _Seifert et al. 2017_ [pdf](https://link.springer.com/chapter/10.1007/978-3-319-54024-5_6)
* How convolutional neural network see the world - A survey of convolutional neural network visualization methods. _Qin et al. 2018_ [pdf](https://arxiv.org/abs/1804.11191) * How convolutional neural network see the world - A survey of convolutional neural network visualization methods. _Qin et al. 2018_ [pdf](https://arxiv.org/abs/1804.11191)
* A brief survey of visualization methods for deep learning models from the perspective of Explainable AI. _Chalkiadakis 2018_ [pdf](https://www.macs.hw.ac.uk/~ic14/IoannisChalkiadakis_RRR.pdf) * A brief survey of visualization methods for deep learning models from the perspective of Explainable AI. _Chalkiadakis 2018_ [pdf](https://www.macs.hw.ac.uk/~ic14/IoannisChalkiadakis_RRR.pdf)
* A Survey Of Methods For Explaining Black Box Models. _Guidotti et al. 2018_ [pdf](https://arxiv.org/pdf/1802.01933.pdf) * A Survey Of Methods For Explaining Black Box Models. _Guidotti et al. 2018_ [pdf](https://arxiv.org/pdf/1802.01933.pdf)
* Understanding Neural Networks via Feature Visualization: A survey. _Nguyen et al. 2019_ [pdf](https://arxiv.org/pdf/1904.08939.pdf) * Understanding Neural Networks via Feature Visualization: A survey. _Nguyen et al. 2019_ [pdf](https://arxiv.org/pdf/1904.08939.pdf)
# Books #### Definitions of Interpretability
* The Mythos of Model Interpretability. _Lipton 2016_ [pdf](https://arxiv.org/abs/1606.03490)
* Towards A Rigorous Science of Interpretable Machine Learning. _Doshi-Velez & Kim. 2017_ [pdf](https://arxiv.org/pdf/1702.08608.pdf)
* Interpretable machine learning: definitions, methods, and applications. _Murdoch et al. 2019_ [pdf](https://arxiv.org/pdf/1901.04592v1.pdf)
#### Books
* A Guide for Making Black Box Models Explainable. _Molnar 2019_ [pdf](https://christophm.github.io/interpretable-ml-book/) * A Guide for Making Black Box Models Explainable. _Molnar 2019_ [pdf](https://christophm.github.io/interpretable-ml-book/)
# A. Explaining inner-workings # A. Explaining inner-workings