Your search
Resource type
Online resource
Results
91 resources-
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
-
McCullagh, P. (2002). What is a statistical model? The Annals of Statistics, 30(5), 1225–1310. https://doi.org/10/bkts3m
-
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
-
Rutten, J. J. M. M. (2000). Universal coalgebra: a theory of systems. Theoretical Computer Science, 249(1), 3–80. https://doi.org/10/fqrjpn
-
Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2017). Understanding deep learning requires rethinking generalization. ArXiv:1611.03530 [Cs]. Retrieved from http://arxiv.org/abs/1611.03530
-
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
-
Hess, K., Dotko, P., Levi, R., Nolte, M., Reimann, M., Scolamiero, M., … Markram, H. (2017). Topological analysis of the connectome of digital reconstructions of neural microcircuits. Frontiers in Computational Neuroscience, 11, 48. https://doi.org/10/gdjbfn
-
Elliott, C. (2018). The simple essence of automatic differentiation. ArXiv:1804.00746 [Cs]. Retrieved from http://arxiv.org/abs/1804.00746
-
Rosen, R. (1958). The representation of biological systems from the standpoint of the theory of categories. The Bulletin of Mathematical Biophysics, 20(4), 317–341. https://doi.org/10/fdgzxz
-
Jacobs, B., & Zanasi, F. (2018). The Logical Essentials of Bayesian Reasoning. ArXiv:1804.01193 [Cs]. Retrieved from http://arxiv.org/abs/1804.01193
-
Boutillier, P., Maasha, M., Li, X., Medina-Abarca, H. F., Krivine, J., Feret, J., … Fontana, W. (2018). The Kappa platform for rule-based modeling. Bioinformatics, 34(13), i583–i592. https://doi.org/10/gdrhw6
-
Ehrhard, T., & Regnier, L. (2003). The differential lambda-calculus. Theoretical Computer Science, 309(1), 1–41. https://doi.org/10/bf3b8v
-
Jacobs, B., & Sprunger, D. (2019). The differential calculus of causal functions. ArXiv:1904.10611 [Cs]. Retrieved from http://arxiv.org/abs/1904.10611
-
Vytiniotis, D., Belov, D., Wei, R., Plotkin, G., & Abadi, M. (2019). The Differentiable Curry. Retrieved from https://openreview.net/forum?id=ryxuz9SzDB
-
Fiore, M., Gambino, N., Hyland, M., & Winskel, G. (2008). The cartesian closed bicategory of generalised species of structures. Journal of the London Mathematical Society, 77(1), 203–220. https://doi.org/10/bd2mr9
-
Staton, S., Yang, H., Heunen, C., Kammar, O., & Wood, F. (2016). Semantics for probabilistic programming: higher-order 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
-
Tix, R., Keimel, K., & Plotkin, G. (2009). Semantic Domains for Combining Probability and Non-Determinism. Electronic Notes in Theoretical Computer Science, 222, 3–99. https://doi.org/10/d9hwq7
-
Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., … Song, D. (2018). Robust Physical-World Attacks on Deep Learning Models. ArXiv:1707.08945 [Cs]. Retrieved from http://arxiv.org/abs/1707.08945
-
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, 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
-
Baudart, G., Mandel, L., Atkinson, E., Sherman, B., Pouzet, M., & Carbin, M. (2019). Reactive Probabilistic Programming. ArXiv:1908.07563 [Cs]. Retrieved from http://arxiv.org/abs/1908.07563
Explore
BIOLOGY, NEUROSCIENCE & PSYCHOLOGY
- Biology (10)
- Neuroscience (7)
- Psychology (3)
CATEGORICAL LOGIC
- Effectus theory (8)
- Linear logic (12)
DIFFERENTIAL CALCULUS
- Differentiation (14)
MACHINE LEARNING
- Machine Learning (28)
MODEL CHECKING AND STATE MACHINES
- Coalgebras (5)
- Rewriting theory (5)
- Symbolic logic (5)
- Transition systems (13)
PROBABILITY & STATISTICS
PROGRAMMING LANGUAGES
- Programming language theory (35)
- Type theory (5)
Methodology
- Compendium (1)
- Implementation (12)
- Sketchy (6)
Topic
- Abstract machines (6)
- Adversarial attacks (5)
- Algebra (1)
- Automatic differentiation (6)
- Bayesian inference (5)
- Bayesianism (11)
- Biology (9)
- Categorical ML (7)
- Categorical probability theory (13)
- Classical ML (13)
- Coalgebras (5)
- Coherence spaces (2)
- Compendium (1)
- Denotational semantics (13)
- Differential Linear Logic (6)
- Differentiation (14)
- Effectus theory (8)
- Emergence (3)
- Game semantics (1)
- Implementation (9)
- Interactive semantics (2)
- Linear logic (11)
- Machine learning (21)
- Neuroscience (6)
- Powerdomains (3)
- Probabilistic programming (15)
- Probabilistic transition systems (4)
- Programming language theory (19)
- Psychology (3)
- Purely theoretical (5)
- Rewriting theory (5)
- Semantics (5)
- Sketchy (4)
- Statistical learning theory (1)
- Symbolic logic (5)
- Systems biology (3)
- Topological data analysis (4)
- Transition systems (5)
- Type theory (2)