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23 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)

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

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

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

Jacobs, B., & Cho, K. (2019). Disintegration and Bayesian Inversion via String Diagrams. Mathematical Structures in Computer Science, 29(7), 938–971. https://doi.org/10/ggdf9v

Elliott, C. (2018). The simple essence of automatic differentiation. ArXiv:1804.00746 [Cs]. Retrieved from http://arxiv.org/abs/1804.00746

Ścibior, A., Kammar, O., & Ghahramani, Z. (2018). Functional programming for modular Bayesian inference. Proceedings of the ACM on Programming Languages, 2(ICFP), 1–29. https://doi.org/10/gft39x

Murfet, D. (2018). dmurfet/deeplinearlogic. Retrieved from https://github.com/dmurfet/deeplinearlogic (Original work published 2016)

Boutillier, P., Maasha, M., Li, X., MedinaAbarca, H. F., Krivine, J., Feret, J., … Fontana, W. (2018). The Kappa platform for rulebased modeling. Bioinformatics, 34(13), i583–i592. https://doi.org/10/gdrhw6

Murfet, D. (2018). dmurfet/polysemantics. Retrieved from https://github.com/dmurfet/polysemantics (Original work published 2016)

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

Cho, K., Jacobs, B., Westerbaan, B., & Westerbaan, A. (2015). An Introduction to Effectus Theory. ArXiv:1512.05813 [QuantPh]. Retrieved from http://arxiv.org/abs/1512.05813

Jacobs, B. (2015). New Directions in Categorical Logic, for Classical, Probabilistic and Quantum Logic. Logical Methods in Computer Science, 11(3), 24. https://doi.org/10/ggdf99

Ścibior, A., Ghahramani, Z., & Gordon, A. D. (2015). Practical Probabilistic Programming with Monads. In Proceedings of the 2015 ACM SIGPLAN Symposium on Haskell (pp. 165–176). New York, NY, USA: ACM. https://doi.org/10/gft39z

Hur, C.K., Nori, A. V., & Rajamani, S. K. (2015). A Provably Correct Sampler for Probabilistic Programs, 21.

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
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