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Culbertson, J., & Sturtz, K. (2013). Bayesian machine learning via category theory. ArXiv:1312.1445 [Math]. Retrieved from http://arxiv.org/abs/1312.1445

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

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

Healy, M. J., & Caudell, T. P. (2004). Neural Networks, Knowledge and Cognition: A Mathematical Semantic Model Based upon Category Theory.

Izbicki, M. (2013). Algebraic classifiers: a generic approach to fast crossvalidation, online training, and parallel training. In ICML.

Jacobs, B. (2018). Categorical Aspects of Parameter Learning. ArXiv:1810.05814 [Cs]. Retrieved from http://arxiv.org/abs/1810.05814

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

Jacobs, B., & Zanasi, F. (2016). A Predicate/State Transformer Semantics for Bayesian Learning. Electronic Notes in Theoretical Computer Science, 325, 185–200. https://doi.org/10/ggdgbb

McCullagh, P. (2002). What is a statistical model? The Annals of Statistics, 30(5), 1225–1310. https://doi.org/10/bkts3m

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

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

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

Murfet, D., & Hu, H. (n.d.). Linear logic and deep learning.

Sprunger, D., & Katsumata, S. (2019). Differentiable Causal Computations via Delayed Trace. In 2019 34th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS) (pp. 1–12). Vancouver, BC, Canada: IEEE. https://doi.org/10/ggdf98
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