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

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

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)

Staton, S. (2017). Commutative Semantics for Probabilistic Programming. In H. Yang (Ed.), Programming Languages and Systems (Vol. 10201, pp. 855–879). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/9783662544341_32

Jacobs, B., & Zanasi, F. (2017). A Formal Semantics of Influence in Bayesian Reasoning. Schloss Dagstuhl  LeibnizZentrum Fuer Informatik GmbH, Wadern/Saarbruecken, Germany. https://doi.org/10/ggdgbc

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

Staton, S., Yang, H., Heunen, C., Kammar, O., & Wood, F. (2016). Semantics for probabilistic programming: higherorder functions, continuous distributions, and soft constraints. Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in Computer Science  LICS ’16, 525–534. https://doi.org/10/ggdf97

Ehrhard, T., Tasson, C., & Pagani, M. (2014). Probabilistic coherence spaces are fully abstract for probabilistic PCF. In Proceedings of the 41st ACM SIGPLANSIGACT Symposium on Principles of Programming Languages  POPL ’14 (pp. 309–320). San Diego, California, USA: ACM Press. https://doi.org/10/ggdf9x

Ehrhard, T., & Danos, V. (2011). Probabilistic coherence spaces as a model of higherorder probabilistic computation. Information and Computation, 209(6), 966–991. https://doi.org/10/ctfch6

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