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34 resources-
Dal Lago, U., & Hoshino, N. (2019). The Geometry of Bayesian Programming (pp. 1–13). https://doi.org/10/ggdk85
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Winn, J. M. (2019). Model-Based Machine Learning. Taylor & Francis Incorporated.
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Wilkinson, D. (2019). A compositional approach to scalable Bayesian computation and probabilistic programming.
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Kerjean, M., & Pacaud Lemay, J.-S. (2019). Higher-Order Distributions for Differential Linear Logic. In M. Bojańczyk & A. Simpson (Eds.), Foundations of Software Science and Computation Structures (pp. 330–347). Cham: Springer International Publishing. https://doi.org/10/ggdmrj
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Heller, M. (2019). Homunculus’ Brain and Categorical Logic. ArXiv, abs/1903.03424.
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Kammar, O., Staton, S., & Vákár, M. (2018). Diffeological Spaces and Denotational Semantics for Differential Programming.
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Ehresmann, A. C. (2018). Applications of Categories to Biology and Cognition. https://doi.org/10/ggdf93
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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
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Hur, C.-K., Nori, A. V., & Rajamani, S. K. (2015). A Provably Correct Sampler for Probabilistic Programs, 21.
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Baez, J. C., & Otter, N. (2015). Operads and Phylogenetic Trees.
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Fages, F. (2014). Cells as Machines: Towards Deciphering Biochemical Programs in the Cell. In R. Natarajan (Ed.), Distributed Computing and Internet Technology (pp. 50–67). Cham: Springer International Publishing. https://doi.org/10/ggdf96
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Izbicki, M. (2013). Algebraic classifiers: a generic approach to fast cross-validation, online training, and parallel training. In ICML.
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Andreatta, M., Ehresmann, A., Guitart, R., & Mazzola, G. (2013). Towards a Categorical Theory of Creativity for Music, Discourse, and Cognition. In J. Yust, J. Wild, & J. A. Burgoyne (Eds.), Mathematics and Computation in Music (pp. 19–37). Berlin, Heidelberg: Springer. https://doi.org/10/ggdndz
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Ehresmann, A. C. (2012). MENS, an Info-Computational Model for (Neuro-)cognitive Systems Capable of Creativity. Entropy, 14, 1703–1716. https://doi.org/10/ggdf9t
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Panangaden, P. (2009). Labelled Markov Processes. London, UK, UK: Imperial College Press.
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Gómez, J. (2009). Modeling cognitive systems with Category Theory Towards rigor in cognitive sciences.
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Danos, V., Feret, J., Fontana, W., Harmer, R., & Krivine, J. (2008). Rule-Based Modelling, Symmetries, Refinements. In J. Fisher (Ed.), Formal Methods in Systems Biology (pp. 103–122). Berlin, Heidelberg: Springer. https://doi.org/10/dc5k68
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Fiore, M. P. (2007). Differential Structure in Models of Multiplicative Biadditive Intuitionistic Linear Logic. In S. R. Della Rocca (Ed.), Typed Lambda Calculi and Applications (pp. 163–177). Berlin, Heidelberg: Springer. https://doi.org/10/c8vgx8
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Wilkinson, D. J. (2006). Stochastic Modelling for Systems Biology. CRC Press.
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Fages, F., Calzone, L., Chabrier-Rivier, N., & Soliman, S. (2006). Machine Learning Biochemical Networks from Temporal Logic Properties. In C. Priami & G. Plotkin (Eds.), Transactions on Computational Systems Biology VI (pp. 68–94). Berlin, Heidelberg: Springer. https://doi.org/10/dd8
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