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MACHINE LEARNING

Machine Learning

Publication year

Between 2000 and 2022

Online resource

yes

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34 resources

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Borchert, T. (2019). amzn/milan. Amazon. Retrieved from https://github.com/amzn/milan (Original work published 2019)
Murfet, D. (2019). dmurfet/2simplicialtransformer. Retrieved from https://github.com/dmurfet/2simplicialtransformer (Original work published 2019)
Hamrick, J. B. (2019). Analogues of mental simulation and imagination in deep learning. Current Opinion in Behavioral Sciences, 29, 8–16. https://doi.org/10.1016/j.cobeha.2018.12.011
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
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
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
Dong, H., Mao, J., Lin, T., Wang, C., Li, L., & Zhou, D. (2019). Neural Logic Machines. ArXiv:1904.11694 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1904.11694
Murfet, D., & Clift, J. (2019). Derivatives of Turing machines in Linear Logic. ArXiv:1805.11813 [Math]. Retrieved from http://arxiv.org/abs/1805.11813
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
Jacobs, B. (2018). Categorical Aspects of Parameter Learning. ArXiv:1810.05814 [Cs]. Retrieved from http://arxiv.org/abs/1810.05814
Murfet, D. (2018). dmurfet/deeplinearlogic. Retrieved from https://github.com/dmurfet/deeplinearlogic (Original work published 2016)
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
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)
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
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
Martin-Maroto, F., & de Polavieja, G. G. (2018). Algebraic Machine Learning. ArXiv:1803.05252 [Cs, Math]. Retrieved from http://arxiv.org/abs/1803.05252
Baydin, A. G., Pearlmutter, B. A., Radul, A. A., & Siskind, J. M. (2018). Automatic differentiation in machine learning: a survey. ArXiv:1502.05767 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1502.05767
Tran, D., Hoffman, M. D., Saurous, R. A., Brevdo, E., Murphy, K., & Blei, D. M. (2017). Deep Probabilistic Programming. ArXiv:1701.03757 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1701.03757
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

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