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Jacobs, B., & Sprunger, D. (2018). Neural Nets via Forward State Transformation and Backward Loss Transformation. ArXiv:1803.09356 [Cs]. Retrieved from http://arxiv.org/abs/1803.09356

MartinMaroto, F., & de Polavieja, G. G. (2018). Algebraic Machine Learning. ArXiv:1803.05252 [Cs, Math]. Retrieved from http://arxiv.org/abs/1803.05252

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

Kammar, O., Staton, S., & Vákár, M. (2018). Diffeological Spaces and Denotational Semantics for Differential Programming.

Jacobs, B. (2018). From probability monads to commutative effectuses. Journal of Logical and Algebraic Methods in Programming, 94, 200–237. https://doi.org/10/gct2wr

Ehrhard, T., & Tasson, C. (2018). Probabilistic call by push value. ArXiv:1607.04690 [Cs]. https://doi.org/10/ggdk8z

Ehresmann, A. C. (2018). Applications of Categories to Biology and Cognition. https://doi.org/10/ggdf93

Castellan, S., Clairambault, P., Paquet, H., & Winskel, G. (2018). The concurrent game semantics of Probabilistic PCF. In Proceedings of the 33rd Annual ACM/IEEE Symposium on Logic in Computer Science  LICS ’18 (pp. 215–224). Oxford, United Kingdom: ACM Press. https://doi.org/10/ggdjfz

Ścibior, A., Kammar, O., Vákár, M., Staton, S., Yang, H., Cai, Y., … Ghahramani, Z. (2017). Denotational validation of higherorder Bayesian inference. Proceedings of the ACM on Programming Languages, 2(POPL), 1–29. https://doi.org/10.1145/3158148

Ehrhard, T., Pagani, M., & Tasson, C. (2017). Measurable Cones and Stable, Measurable Functions. Proceedings of the ACM on Programming Languages, 2(POPL), 1–28. https://doi.org/10/ggdjf8

Jacobs, B. (2017). Quantum effect logic in cognition. Journal of Mathematical Psychology, 81, 1–10. https://doi.org/10/gcnkcj

Mascari, J.F., Giacchero, D., & Sfakianakis, N. (2017). Symetries and asymetries of the immune system response: A categorification approach. In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 1451–1454). https://doi.org/10/ggdnd3

Hess, K., Dotko, P., Levi, R., Nolte, M., Reimann, M., Scolamiero, M., … Markram, H. (2017). Topological analysis of the connectome of digital reconstructions of neural microcircuits. Frontiers in Computational Neuroscience, 11, 48. https://doi.org/10/gdjbfn

Tran, D., Hoffman, M. D., Saurous, R. A., Brevdo, E., Murphy, K., & Blei, D. M. (2017). Deep Probabilistic Programming. ArXiv:1701.03757 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1701.03757

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

Borgström, J., Lago, U. D., Gordon, A. D., & Szymczak, M. (2017). A LambdaCalculus Foundation for Universal Probabilistic Programming. ArXiv:1512.08990 [Cs]. Retrieved from http://arxiv.org/abs/1512.08990

Heunen, C., Kammar, O., Staton, S., & Yang, H. (2017). A Convenient Category for HigherOrder Probability Theory. ArXiv:1701.02547 [Cs, Math]. Retrieved from http://arxiv.org/abs/1701.02547

Clerc, F., Danos, V., Dahlqvist, F., & Garnier, I. (2017). Pointless learning (long version). Retrieved from https://hal.archivesouvertes.fr/hal01429663

Staton, S. (2017). Commutative Semantics for Probabilistic Programming. In H. Yang (Ed.), Programming Languages and Systems (Vol. 10201, pp. 855–879). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/9783662544341_32
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