Resources - AWS Prescriptive Guidance
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Resources

References

  1. Adadi, Amina and Mohammed Berrada. 2018. “Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI).” IEEE Access 6: 52138–52160.

  2. Ancona, Marco, Enea Ceolini, Cengiz Oztireli, and Markus Gross. 2018. “Towards better understanding of gradient-based attribution methods for Deep Neural Networks.” Proceedings of the International Conference on Learning Representations (ICLR). arXiv:1711.06104.

  3. Dhamdhere, Kedar, Mukund Sundararajan, and Qiqi Yan. 2018. “How Important Is a Neuron?” Proceedings of the Thirty-sixth International Conference on Machine Learning (ICML). arXiv:1805.12233.

  4. Dua, Dheeru and Casey Graff. 2019. UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

  5. Kapishnikov, Andrei, Tolga Bolukbasi, Fernanda Viegas, and Michael Terry. 2019. “XRAI: Better Attributions Through Regions.” Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV): 4948–4957. arXiv:1906.02825.

  6. Kim, Been, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres. 2018. “Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV).” arXiv:1711.11279.

  7. Lundberg, Scott M., Gabriel G. Erion, and Su-In Lee. 2019. “Consistent Individualized Feature Attribution for Tree Ensembles.” arXiv:1802.03888.

  8. Lundberg, Scott M. and Su-In Lee. 2017. “A Unified Approach to Interpreting Model Predictions”. Advances in Neural Information Processing Systems (NIPS) 30. arXiv:1705.07874.

  9. Rajpurkar, Pranav, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. “SQuAD: 100,000+ Questions for Machine Comprehension of Text.” arXiv:1606.05250.

  10. Ribeiro, Marco T., Sameer Singh, and Carlos Guestrin. 2016. "’Why Should I Trust You?’: Explaining the Predictions of Any Classifier.” KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: 1135–1144. arXiv:1602.04938.

  11. Sundararajan, Mukund, Ankur Taly, and Qiqi Yan. 2017. “Axiomatic Attribution for Deep Networks.” Proceedings of the 34th International Conference on Machine Learning 70: 3319–3328. arXiv:1703.01365.

External software packages

Additional reading