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Heunen, C., Kammar, O., Staton, S., & Yang, H. (2017). A Convenient Category for Higher-Order Probability Theory. ArXiv:1701.02547 [Cs, Math]. Retrieved from http://arxiv.org/abs/1701.02547
Blute, R., Ehrhard, T., & Tasson, C. (2010). A convenient differential category. ArXiv:1006.3140 [Cs, Math]. Retrieved from http://arxiv.org/abs/1006.3140
Vákár, M., Kammar, O., & Staton, S. (2018). A Domain Theory for Statistical Probabilistic Programming. ArXiv:1811.04196 [Cs]. Retrieved from http://arxiv.org/abs/1811.04196
Jacobs, B., & Zanasi, F. (2017). A Formal Semantics of Influence in Bayesian Reasoning. Schloss Dagstuhl - Leibniz-Zentrum Fuer Informatik GmbH, Wadern/Saarbruecken, Germany. https://doi.org/10/ggdgbc
Bartels, F., Sokolova, A., & de Vink, E. (2003). A hierarchy of probabilistic system types. Electronic Notes in Theoretical Computer Science, 82(1), 57–75. https://doi.org/10/d7kq38
Borgström, J., Lago, U. D., Gordon, A. D., & Szymczak, M. (2017). A Lambda-Calculus Foundation for Universal Probabilistic Programming. ArXiv:1512.08990 [Cs]. Retrieved from http://arxiv.org/abs/1512.08990
Jacobs, B., & Zanasi, F. (2016). A Predicate/State Transformer Semantics for Bayesian Learning. Electronic Notes in Theoretical Computer Science, 325, 185–200. https://doi.org/10/ggdgbb
Fritz, T., & Perrone, P. (2019). A Probability Monad as the Colimit of Spaces of Finite Samples. ArXiv:1712.05363 [Cs, Math]. Retrieved from http://arxiv.org/abs/1712.05363
Manzyuk, O. (2012). A Simply Typed λ-Calculus of Forward Automatic Differentiation. Electronic Notes in Theoretical Computer Science, 286, 257–272. https://doi.org/10/ggdm57
Heckerman, D. (1995). A Tutorial on Learning With Bayesian Networks. Retrieved from https://www.microsoft.com/en-us/research/publication/a-tutorial-on-learning-with-bayesian-networks/
Jacobs, B., & Adams, R. (2015). A Type Theory for Probabilistic and Bayesian Reasoning. ArXiv:1511.09230 [Cs, Math]. Retrieved from http://arxiv.org/abs/1511.09230
Lack, S., & Sobociński, P. (2005). Adhesive and quasiadhesive categories. RAIRO - Theoretical Informatics and Applications - Informatique Théorique et Applications, 39(3), 511–545. https://doi.org/10/fntwvv
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
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
Martin-Maroto, F., & de Polavieja, G. G. (2018). Algebraic Machine Learning. ArXiv:1803.05252 [Cs, Math]. Retrieved from http://arxiv.org/abs/1803.05252
Ehrhard, T. (2016). An introduction to Differential Linear Logic: proof-nets, models and antiderivatives. ArXiv:1606.01642 [Cs]. Retrieved from http://arxiv.org/abs/1606.01642
Cho, K., Jacobs, B., Westerbaan, B., & Westerbaan, A. (2015). An Introduction to Effectus Theory. ArXiv:1512.05813 [Quant-Ph]. Retrieved from http://arxiv.org/abs/1512.05813
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
Olah, C., & Carter, S. (2016). Attention and Augmented Recurrent Neural Networks. Distill, 1(9), e1. https://doi.org/10/gf33sg
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

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