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Recursos
Referencias
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Adadi, Amina y Mohammed Berrada. 2018. “Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI).” IEEE Access 6: 52138–52160.
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Ancona, Marco, Enea Ceolini, Cengiz Oztireli y 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
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Dhamdhere, Kedar, Mukund Sundararajan y Qiqi Yan. 2018. “How Important Is a Neuron?” Proceedings of the Thirty-sixth International Conference on Machine Learning (ICML). arXiv:1805.12233
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Dua, Dheeru y Casey Graff. 2019. UCI Machine Learning Repository [http://archive.ics.uci.edu/ml
]. Irvine, CA: University of California, School of Information and Computer Science. -
Kapishnikov, Andrei, Tolga Bolukbasi, Fernanda Viegas y Michael Terry. 2019. “XRAI: Better Attributions Through Regions.” Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV): 4948–4957. arXiv:1906.02825
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Kim, Been, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas y Rory Sayres. 2018. “Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV).” arXiv:1711.11279
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Lundberg, Scott M., Gabriel G. Erion y Su-In Lee. 2019. “Consistent Individualized Feature Attribution for Tree Ensembles.” arXiv:1802.03888
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Lundberg, Scott M. y Su-In Lee. 2017. “A Unified Approach to Interpreting Model Predictions”. Advances in Neural Information Processing Systems (NIPS) 30. arXiv:1705.07874
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Rajpurkar, Pranav, Jian Zhang, Konstantin Lopyrev y Percy Liang. 2016. “SQuAD: 100,000+ Questions for Machine Comprehension of Text.” arXiv:1606.05250
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Ribeiro, Marco T., Sameer Singh y 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
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Sundararajan, Mukund, Ankur Taly y Qiqi Yan. 2017. “Axiomatic Attribution for Deep Networks.” Proceedings of the 34th International Conference on Machine Learning 70: 3319–3328. arXiv:1703.01365
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External software packages
Lecturas adicionales
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Amazon SageMaker Clarify Model Explainability (documentación de SageMaker)
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Repositorio Amazon SageMaker Clarify
(GitHub) -
Molnar, Christoph. Interpretable machine learning. A Guide for Making Black Box Models Explainable
, 2019.