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Battaglia, P. W., Hamrick, J. B., Bapst, V., SanchezGonzalez, A., Zambaldi, V., Malinowski, M., … Pascanu, R. (2018). Relational inductive biases, deep learning, and graph networks. ArXiv:1806.01261 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1806.01261

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

Borchert, T. (2019). amzn/milan. Amazon. Retrieved from https://github.com/amzn/milan (Original work published 2019)

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

Culbertson, J., & Sturtz, K. (2013). Bayesian machine learning via category theory. ArXiv:1312.1445 [Math]. Retrieved from http://arxiv.org/abs/1312.1445

Dong, H., Mao, J., Lin, T., Wang, C., Li, L., & Zhou, D. (2019). Neural Logic Machines. ArXiv:1904.11694 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1904.11694

Engeler, E. (1995). The Combinatory Programme. Birkhäuser Basel. https://doi.org/10.1007/9781461242680

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

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

Fong, B., Spivak, D. I., & Tuyéras, R. (2019). Backprop as Functor: A compositional perspective on supervised learning. ArXiv:1711.10455 [Cs, Math]. Retrieved from http://arxiv.org/abs/1711.10455

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

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

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

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

Hamrick, J. B. (2019). Analogues of mental simulation and imagination in deep learning. Current Opinion in Behavioral Sciences, 29, 8–16. https://doi.org/10.1016/j.cobeha.2018.12.011

Harris, K. D. (2019). Characterizing the invariances of learning algorithms using category theory. ArXiv:1905.02072 [Cs, Math, Stat]. Retrieved from http://arxiv.org/abs/1905.02072

Healy, M. J., & Caudell, T. P. (2004). Neural Networks, Knowledge and Cognition: A Mathematical Semantic Model Based upon Category Theory.

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

Izbicki, M. (2013). Algebraic classifiers: a generic approach to fast crossvalidation, online training, and parallel training. In ICML.

Jacobs, B. (2018). Categorical Aspects of Parameter Learning. ArXiv:1810.05814 [Cs]. Retrieved from http://arxiv.org/abs/1810.05814
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