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146 resources
Keimel, K., & Plotkin, G. D. (2017). Mixed powerdomains for probability and nondeterminism. ArXiv:1612.01005 [Cs]. https://doi.org/10/ggdmrp

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

Hess, K., Reimann, M. W., Nolte, M., Scolamiero, M., Turner, K., Perin, R., … Markram, H. (2017). Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function. Frontiers in Computational Neuroscience, 11. https://doi.org/10/gdjbfn

Sizemore, A., Giusti, C., Kahn, A., Betzel, R. F., & Bassett, D. S. (2016). Cliques and Cavities in the Human Connectome. ArXiv:1608.03520 [Math, qBio]. Retrieved from http://arxiv.org/abs/1608.03520

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

Olah, C., & Carter, S. (2016). Attention and Augmented Recurrent Neural Networks. Distill, 1(9), e1. https://doi.org/10/gf33sg

Serafini, L., & Garcez, A. d’Avila. (2016). Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge. ArXiv:1606.04422 [Cs]. Retrieved from http://arxiv.org/abs/1606.04422

Ehrhard, T. (2016). An introduction to Differential Linear Logic: proofnets, models and antiderivatives. ArXiv:1606.01642 [Cs]. Retrieved from http://arxiv.org/abs/1606.01642

Tsuchiya, N., Taguchi, S., & Saigo, H. (2016). Using category theory to assess the relationship between consciousness and integrated information theory. Neuroscience Research, 107, 1–7. https://doi.org/10/ggdf95

Hess, K., Kanari, L., Dłotko, P., Scolamiero, M., Levi, R., Shillcock, J., & Markram, H. (2016). Quantifying topological invariants of neuronal morphologies. ArXiv:1603.08432 [Cs, Math, qBio]. Retrieved from http://arxiv.org/abs/1603.08432

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

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

Ehresmann, A. C., & GomezRamirez, J. (2015). Conciliating neuroscience and phenomenology via category theory. Progress in Biophysics and Molecular Biology, 119(3), 347–359. https://doi.org/10/f75jzr

Jacobs, B., & Adams, R. (2015). A Type Theory for Probabilistic and Bayesian Reasoning. ArXiv:1511.09230 [Cs, Math]. Retrieved from http://arxiv.org/abs/1511.09230

Jacobs, B., & Furber, R. (2015). Towards a Categorical Account of Conditional Probability. Electronic Proceedings in Theoretical Computer Science, 195, 179–195. https://doi.org/10/ggdf9w

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

Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and Harnessing Adversarial Examples. ArXiv:1412.6572 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1412.6572

Paul, A., & Venkatasubramanian, S. (2015). Why does Deep Learning work?  A perspective from Group Theory. ArXiv:1412.6621 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1412.6621

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.
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