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Winn, J. M. (2019). ModelBased Machine Learning. Taylor & Francis Incorporated.

Brown, T. B., Mané, D., Roy, A., Abadi, M., & Gilmer, J. (2018). Adversarial Patch. ArXiv:1712.09665 [Cs]. Retrieved from http://arxiv.org/abs/1712.09665

Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., … Song, D. (2018). Robust PhysicalWorld Attacks on Deep Learning Models. ArXiv:1707.08945 [Cs]. Retrieved from http://arxiv.org/abs/1707.08945

Baydin, A. G., Pearlmutter, B. A., Radul, A. A., & Siskind, J. M. (2018). Automatic differentiation in machine learning: a survey. ArXiv:1502.05767 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1502.05767

Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2017). Understanding deep learning requires rethinking generalization. ArXiv:1611.03530 [Cs]. Retrieved from http://arxiv.org/abs/1611.03530

Kurakin, A., Goodfellow, I., & Bengio, S. (2017). Adversarial examples in the physical world. ArXiv:1607.02533 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1607.02533

Olah, C., & Carter, S. (2016). Attention and Augmented Recurrent Neural Networks. Distill, 1(9), e1. https://doi.org/10/gf33sg

Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and Harnessing Adversarial Examples. ArXiv:1412.6572 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1412.6572

Paul, A., & Venkatasubramanian, S. (2015). Why does Deep Learning work?  A perspective from Group Theory. ArXiv:1412.6621 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1412.6621

Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452–459. https://doi.org/10/gdxwhq

Graves, A., Wayne, G., & Danihelka, I. (2014). Neural Turing Machines. ArXiv:1410.5401 [Cs]. Retrieved from http://arxiv.org/abs/1410.5401

Goodfellow, I. J., PougetAbadie, J., Mirza, M., Xu, B., WardeFarley, D., Ozair, S., … Bengio, Y. (2014). Generative Adversarial Networks. ArXiv:1406.2661 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1406.2661

Poggio, T. (2013). Tomaso A. Poggio autobiography (p. 54). Retrieved from http://poggiolab.mit.edu/sites/default/files/cv/tomasopoggio.pdf

Fages, F., Calzone, L., ChabrierRivier, N., & Soliman, S. (2006). Machine Learning Biochemical Networks from Temporal Logic Properties. In C. Priami & G. Plotkin (Eds.), Transactions on Computational Systems Biology VI (pp. 68–94). Berlin, Heidelberg: Springer. https://doi.org/10/dd8

Wermuth, N., & Cox, D. R. (2001). Graphical Models: Overview. In N. J. Smelser & P. B. Baltes (Eds.), International Encyclopedia of the Social & Behavioral Sciences (pp. 6379–6386). Oxford: Pergamon. https://doi.org/10.1016/B0080430767/00440X

Heckerman, D. (1995). A Tutorial on Learning With Bayesian Networks. Retrieved from https://www.microsoft.com/enus/research/publication/atutorialonlearningwithbayesiannetworks/

Siegelmann, H. T., & Sontag, E. D. (1995). On the Computational Power of Neural Nets. Journal of Computer and System Sciences, 50(1), 132–150. https://doi.org/10/dvwtc3

Murfet, D. (n.d.). Mathematics of AlphaGo.

Murfet, D. (n.d.). Algebra and Artiﬁcial Intelligence.
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