- Altmann, A., Toloşi, L., Sander, O. and Lengauer T. Permutation importance: A corrected feature importance measure. Bioinformatics 26, 10 (2010), 1340--1347.Google ScholarCross Ref
- Ancona, M., Ceolini, E., Oztireli, C. and Gross, M. Towards better understanding of gradient-based attribution methods for deep neural networks. In Proceedings of the Intern. Conf. Learning Representations, 2018.Google Scholar
- Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller K.-R. and Samek, W. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS One 10, 7 (2015), e0130140.Google ScholarCross Ref
- Bahdanau, D., Cho, K. and Bengio, Y. Neural machine translation by jointly learning to align and translate. In Proceedings of the Intern. Conf. Learning Representations, 2015.Google Scholar
- Bastani, O., Kim, C., and Bastani, H. Interpretability via model extraction. In Proceedings of the Fairness, Accountability, and Transparency in Machine Learning Workshop, 2017.Google Scholar
- Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M. and Elhadad, N. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In Proceedings of the ACM SIGKDD Intern. Conf. Knowledge Discovery and Data Mining. ACM, 2015.Google ScholarDigital Library
- Chen, T. and Guestrin, C. Xgboost: A scalable tree boosting system. In Proceedings of the ACM SIGKDD Intern. Conf. Knowledge Discovery and Data Mining. ACM, 2016.Google ScholarDigital Library
- Dabkowski, P. and Gal, Y. Real time image saliency for black box classifiers. Advances in Neural Information Processing Systems (2017), 6970--6979.Google Scholar
- Dix, A. Human issues in the use of pattern recognition techniques. Neural Networks and Pattern Recognition in Human Computer Interaction (1992), 429--451.Google Scholar
- Doshi-Velez, F. and Kim, B. Towards a rigorous science of interpretable machine learning. 2017.Google Scholar
- Du, M., Liu, N., Song, Q. and Hu, X. Towards explanation of DNN-based prediction with guided feature inversion. In Proceedings of the ACM SIGKDD Intern. Conf. Knowledge Discovery and Data Mining, 2018.Google ScholarDigital Library
- Du, M., Liu, N., Yang, F. and Hu, X. On attribution of recurrent neural network predictions via additive decomposition. In Proceedings of the WWW Conf., 2019.Google ScholarDigital Library
- Fong, R. and Vedaldi, A. Interpretable explanations of black boxes by meaningful perturbation. In Proceedings of the Intern. Conf. Computer Vision, 2017.Google ScholarCross Ref
- Freitas, A.A. Comprehensible classification models: A position paper. ACM SIGKDD Explorations Newsletter, 2014.Google ScholarDigital Library
- Goodfellow, I., Bengio, Y and Courville, A. Deep Learning, Vol.1. MIT Press, Cambridge, MA, 2016.Google ScholarDigital Library
- Goodfellow, I.J., Shlens, J. and Szegedy, C. Explaining and harnessing adversarial examples. In Proceedings of the Intern. Conf. Learning Representations, 2015.Google Scholar
- Kádár, A., Chrupa-la, G., and Alishahi, A. Representation of linguistic form and function in recurrent neural networks. Computational Linguistics 43, 4 (2017), 761--780.Google ScholarDigital Library
- Karpathy, A., Johnson, J., and Fei-Fei, L. Visualizing and understanding recurrent networks. In Proceedings of the ICLR Workshop, 2016.Google Scholar
- Liu, N., Du, M., and Hu, X. Representation interpretation with spatial encoding and multimodal analytics. In Proceedings of the ACM Intern. Conf. Web Search and Data Mining, 2019.Google ScholarDigital Library
- Liu, N., Yang, H., and Hu, X. Adversarial detection with model interpretation. In Proceedings of the ACM SIGKDD Intern. Conf. Knowledge Discovery and Data Mining, 2018.Google ScholarDigital Library
- McCullagh, P. and Nelder, J.A. Generalized Linear M, Vol. 37. CRC Press, 1989.Google ScholarCross Ref
- Miller, T. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence (2018).Google Scholar
- Molnar, C. Interpretable Machine Learning (2018); https://christophm.github.io/interpretable-ml-book/.Google Scholar
- Mudrakarta, P.K., Taly, A., Sundararajan, M. and Dhamdhere, K. Did the model understand the question? In Proceedings of the 56th Annual Meeting of the Assoc. Computational Linguistics, 2018.Google ScholarCross Ref
- Nguyen, A., Dosovitskiy, A., Yosinski, J., Brox, T. and Clune, J. Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. Advances in Neural Information Processing Systems, 2016.Google ScholarDigital Library
- Nguyen, A., Yosinski, J. and Clune, J. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In Proceedings of the IEEE Conf. Computer Vision and Pattern Recognition, 2015.Google ScholarCross Ref
- Nguyen, A., Yosinski, J. and Clune, J. Multifaceted feature visualization: Uncovering the different types of features learned by each neuron in deep neural networks. In Proceedings of the ICLR Workshop, 2016.Google Scholar
- Peters, M.E. et al. Deep contextualized word representations. In Proceedings of the NAACL-HLT, 2018.Google Scholar
- Quinlan, J.R. Simplifying decision trees. Intern. J. Man-Machine Studies 27, 3 (1987), 221--234.Google ScholarDigital Library
- Ribeiro, M.T., Singh, S. and Guestrin, C. Why should I trust you? Explaining the predictions of any classifier. In Proceedings of the ACM SIGKDD Intern. Conf. Knowledge Discovery and Data Mining, 2016.Google ScholarCross Ref
- Ribeiro, M.T., Singh, S. and Guestrin, C. Anchors: High-precision model-agnostic explanations. In Proceedings of the AAAI Conf. Artificial Intelligence, 2018.Google Scholar
- Sabour, S., Frosst, N. and Hinton, G.E. Dynamic routing between capsules. Advances in Neural Information Processing Systems, 2017.Google Scholar
- Simonyan, K., Vedaldi, A. and Zisserman, A. Deep inside convolutional networks: Visualising image classification models and saliency maps. In Proceedings of the ICLR Workshop, 2014.Google Scholar
- Springenberg, J.T., Dosovitskiy, A., Brox, T. and Riedmiller, M. Striving for simplicity: The all convolutional net. In Proceedings of the ICLR workshop, 2015.Google Scholar
- Tomsett, R., Braines, D., Harborne, D., Preece, A. and Chakraborty, S. Interpretable to whom? A role-based model for analyzing interpretable machine learning systems. In Proceedings of the ICML Workshop on Human Interpretability in Machine Learning, 2018.Google Scholar
- Vandewiele, G., Janssens, G., Ongenae, O., and Van Hoecke, F.S. Genesim: Genetic extraction of a single, interpretable model. In Proceedings of the NIPS Workshop, 2016.Google Scholar
- Wachter, S., Mittelstadt, B. and Russell, C. Counterfactual explanations without opening the black box: Automated decisions and the GDPR. 2017.Google Scholar
- Xu, K. et al. Show, attend and tell: Neural image caption generation with visual attention. In Proceedings of the Intern. Conf. Machine Learning, 2015.Google Scholar
- Zhang, Q., Wu, Y.N. and Zhu, S.-C. Interpretable convolutional neural networks. In Proceedings of the IEEE Conf. Computer Vision and Pattern Recognition, 2018.Google ScholarCross Ref
- Zhou, B., Khosla, A., Lapedriza, A., Oliva, A. and Torralba, A. Object detectors emerge in deep scene CNNs. In Proceedings of the Intern. Conf. Learning Representations, 2015.Google Scholar
Index Terms
- Techniques for interpretable machine learning
Recommendations
Interpretable Machine Learning in Healthcare
BCB '18: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health InformaticsThis tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning ...
Machine Learning: The State of the Art
The two fundamental problems in machine learning (ML) are statistical analysis and algorithm design. The former tells us the principles of the mathematical models that we establish from the observation data. The latter defines the conditions on which ...
Comments