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104 resources

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Abramsky, S., Haghverdi, E., & Scott, P. (2002). Geometry of Interaction and Linear Combinatory Algebras. Mathematical. Structures in Comp. Sci., 12(5), 625–665. https://doi.org/10/fcsmhm
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
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, 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
Baudart, G., Mandel, L., Atkinson, E., Sherman, B., Pouzet, M., & Carbin, M. (2019). Reactive Probabilistic Programming. ArXiv:1908.07563 [Cs]. Retrieved from http://arxiv.org/abs/1908.07563
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
Blute, R. F., Cockett, J. R. B., Lemay, J.-S. P., & Seely, R. A. G. (2019). Differential Categories Revisited. Applied Categorical Structures. https://doi.org/10/ggdm44
Blute, R., Ehrhard, T., & Tasson, C. (2010). A convenient differential category. ArXiv:1006.3140 [Cs, Math]. Retrieved from http://arxiv.org/abs/1006.3140
Borchert, T. (2019). amzn/milan. Amazon. Retrieved from https://github.com/amzn/milan (Original work published 2019)
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
Boutillier, P., Maasha, M., Li, X., Medina-Abarca, H. F., Krivine, J., Feret, J., … Fontana, W. (2018). The Kappa platform for rule-based modeling. Bioinformatics, 34(13), i583–i592. https://doi.org/10/gdrhw6
Brown, R., & Porter, T. (2008). Category Theory and Higher Dimensional Algebra: potential descriptive tools in neuroscience. ArXiv:Math/0306223. Retrieved from http://arxiv.org/abs/math/0306223
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
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
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
Clerc, F., Danos, V., Dahlqvist, F., & Garnier, I. (2017). Pointless learning (long version). Retrieved from https://hal.archives-ouvertes.fr/hal-01429663
Culbertson, J., & Sturtz, K. (2013). Bayesian machine learning via category theory. ArXiv:1312.1445 [Math]. Retrieved from http://arxiv.org/abs/1312.1445
Desharnais, J., Edalat, A., & Panangaden, P. (2002). Bisimulation for Labelled Markov Processes. Information and Computation, 179(2), 163–193. https://doi.org/10/fmp9vd
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
Ehresmann, A. C., & Gomez-Ramirez, J. (2015). Conciliating neuroscience and phenomenology via category theory. Progress in Biophysics and Molecular Biology, 119(3), 347–359. https://doi.org/10/f75jzr
Ehrhard, T., & Regnier, L. (2006). Differential interaction nets. Theoretical Computer Science, 364(2), 166–195. https://doi.org/10/bg5g4b

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