[49] Wu Lin, Mohammad Emtiyaz Khan, and Mark Schmidt. Stein's lemma for the reparameterization trick with exponential family mixtures, 2019. URL https://arxiv.org/abs/1910.13398.
[50] Zachary C Lipton. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue, 16(3):31-57, 2018.
[51] Jun S Liu. Siegel's formula via stein's identities. Statistics & probability letters, 21(3), 1994-10. ISSN 0167-7152.
[52] Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016.
[53] Scott M Lundberg and Su-In Lee. A unified approach to interpreting model predictions. Advances in neural information processing systems, 30, 2017.
[54] Scott M Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, and Su-In Lee. From local explanations to global understanding with explainable ai for trees. Nature machine intelligence, 2(1):56-67, 2020.
[55] Andrew Z Luo, Eric Whitmire, James W Stout, Drew Martenson, and Shwetak Patel. Automatic characterization of user errors in spirometry. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 4239-4242. IEEE, 2017.
[56] Aria Masoomi, Chieh Wu, Tingting Zhao, Zifeng Wang, Peter Castaldi, and Jennifer Dy. Instance-wise feature grouping. Advances in Neural Information Processing Systems, 33: 13374-13386, 2020.
[57] Aria Masoomi, Davin Hill, Zhonghui Xu, Craig P Hersh, Edwin K Silverman, Peter J Castaldi, Stratis Ioannidis, and Jennifer Dy. Explanations of black-box models based on directional feature interactions. In International Conference on Learning Representations, 2021.
[58] Guido F Montufar, Razvan Pascanu, Kyunghyun Cho, and Yoshua Bengio. On the number of linear regions of deep neural networks. Advances in neural information processing systems, 27, 2014.
[59] V.C. Moore. Spirometry: step by step. Breathe, 8(3):232-240, 2012. ISSN 1810-6838. doi: 10. 1183/20734735.0021711. URL https://breathe.ersjournals.com/content/8/3/232.
[60] Yurii Nesterov and Vladimir Spokoiny. Random gradient-free minimization of convex functions. Foundations of Computational Mathematics, 17:527-566, 2017.
[61] Daniel Omeiza, Skyler Speakman, Celia Cintas, and Komminist Weldermariam. Smooth gradcam++: An enhanced inference level visualization technique for deep convolutional neural network models. arXiv preprint arXiv:1908.01224, 2019.
[62] Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in pytorch. 2017.
[63] Hanchuan Peng, Fuhui Long, and Chris Ding. Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell., 27(8):1226-1238, aug 2005. ISSN 0162-8828. doi: 10.1109/TPAMI.2005.159. URL https://doi.org/10.1109/TPAMI.2005.159.
[64] Maithra Raghu, Ben Poole, Jon Kleinberg, Surya Ganguli, and Jascha Sohl-Dickstein. On the expressive power of deep neural networks. In international conference on machine learning, pages 2847-2854. PMLR, 2017.
[65] Prajit Ramachandran, Barret Zoph, and Quoc V Le. Searching for activation functions. arXiv preprint arXiv:1710.05941, 2017.