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MACHINE LEARNING

Machine Learning

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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
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
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
Culbertson, J., & Sturtz, K. (2013). Bayesian machine learning via category theory. ArXiv:1312.1445 [Math]. Retrieved from http://arxiv.org/abs/1312.1445
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
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
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
Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452–459. https://doi.org/10/gdxwhq
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … Bengio, Y. (2014). Generative Adversarial Networks. ArXiv:1406.2661 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1406.2661
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
Graves, A., Wayne, G., & Danihelka, I. (2014). Neural Turing Machines. ArXiv:1410.5401 [Cs]. Retrieved from http://arxiv.org/abs/1410.5401
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
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
Heckerman, D. (1995). A Tutorial on Learning With Bayesian Networks. Retrieved from https://www.microsoft.com/en-us/research/publication/a-tutorial-on-learning-with-bayesian-networks/
Jacobs, B. (2018). Categorical Aspects of Parameter Learning. ArXiv:1810.05814 [Cs]. Retrieved from http://arxiv.org/abs/1810.05814
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
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
Kurakin, A., Goodfellow, I., & Bengio, S. (2017). Adversarial examples in the physical world. ArXiv:1607.02533 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1607.02533
Martin-Maroto, F., & de Polavieja, G. G. (2018). Algebraic Machine Learning. ArXiv:1803.05252 [Cs, Math]. Retrieved from http://arxiv.org/abs/1803.05252
McCullagh, P. (2002). What is a statistical model? The Annals of Statistics, 30(5), 1225–1310. https://doi.org/10/bkts3m

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