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

Murfet, D. (2019). dmurfet/2simplicialtransformer. Retrieved from https://github.com/dmurfet/2simplicialtransformer (Original work published 2019)

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

Vytiniotis, D., Belov, D., Wei, R., Plotkin, G., & Abadi, M. (2019). The Differentiable Curry. Retrieved from https://openreview.net/forum?id=ryxuz9SzDB

Murfet, D., Clift, J., Doryn, D., & Wallbridge, J. (2019). Logic and the $2$Simplicial Transformer. ArXiv:1909.00668 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1909.00668

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

Wilkinson, D. (2019, August 7). Write your own generalpurpose monadic probabilistic programming language from scratch in 50 lines of (Scala) code. Retrieved November 27, 2019, from https://darrenjw.wordpress.com/2019/08/07/writeyourowngeneralpurposemonadicprobabilisticprogramminglanguagefromscratchin50linesofscalacode/

Law, J., & Wilkinson, D. (2019). Functional probabilistic programming for scalable Bayesian modelling. ArXiv:1908.02062 [Stat]. Retrieved from http://arxiv.org/abs/1908.02062

Jacobs, B., Kissinger, A., & Zanasi, F. (2019). Causal Inference by String Diagram Surgery. ArXiv:1811.08338 [Cs, Math]. Retrieved from http://arxiv.org/abs/1811.08338

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

Dal Lago, U., & Hoshino, N. (2019). The Geometry of Bayesian Programming (pp. 1–13). https://doi.org/10/ggdk85

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

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

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

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

Jacobs, B., & Sprunger, D. (2019). The differential calculus of causal functions. ArXiv:1904.10611 [Cs]. Retrieved from http://arxiv.org/abs/1904.10611

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

Ehrhard, T. (2019). Differentials and distances in probabilistic coherence spaces. ArXiv:1902.04836 [Cs]. Retrieved from http://arxiv.org/abs/1902.04836

Murfet, D., & Clift, J. (2019). Derivatives of Turing machines in Linear Logic. ArXiv:1805.11813 [Math]. Retrieved from http://arxiv.org/abs/1805.11813

Wilkinson, D. (2019). A compositional approach to scalable Bayesian computation and probabilistic programming.
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