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146 resources
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

Kerjean, M., & Pacaud Lemay, J.S. (2019). HigherOrder 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

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

Heller, M. (2019). Homunculus’ Brain and Categorical Logic. ArXiv, abs/1903.03424.

Tuyéras, R. (2018). Category theory for genetics I: mutations and sequence alignments. ArXiv:1805.07002 [Math]. Retrieved from http://arxiv.org/abs/1805.07002

Paquet, H., & Winskel, G. (2018). Continuous Probability Distributions in Concurrent Games. Electronic Notes in Theoretical Computer Science, 341, 321–344. https://doi.org/10/ggdmwv

Vákár, M., Kammar, O., & Staton, S. (2018). A Domain Theory for Statistical Probabilistic Programming. ArXiv:1811.04196 [Cs]. Retrieved from http://arxiv.org/abs/1811.04196

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

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

Tuyéras, R. (2018). Category theory for genetics II: genotype, phenotype and haplotype. ArXiv:1805.07004 [Math]. Retrieved from http://arxiv.org/abs/1805.07004

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

Fages, F., Martinez, T., Rosenblueth, D. A., & Soliman, S. (2018). Influence Networks Compared with Reaction Networks: Semantics, Expressivity and Attractors. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 15(4), 1138–1151. https://doi.org/10/ggdf94

Battaglia, P. W., Hamrick, J. B., Bapst, V., SanchezGonzalez, A., Zambaldi, V., Malinowski, M., … Pascanu, R. (2018). Relational inductive biases, deep learning, and graph networks. ArXiv:1806.01261 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1806.01261

Tuyéras, R. (2018). Category Theory for Genetics. ArXiv:1708.05255 [Math]. Retrieved from http://arxiv.org/abs/1708.05255

Brown, T. B., Mané, D., Roy, A., Abadi, M., & Gilmer, J. (2018). Adversarial Patch. ArXiv:1712.09665 [Cs]. Retrieved from http://arxiv.org/abs/1712.09665

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

Jacobs, B., & Zanasi, F. (2018). The Logical Essentials of Bayesian Reasoning. ArXiv:1804.01193 [Cs]. Retrieved from http://arxiv.org/abs/1804.01193

Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., … Song, D. (2018). Robust PhysicalWorld Attacks on Deep Learning Models. ArXiv:1707.08945 [Cs]. Retrieved from http://arxiv.org/abs/1707.08945
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