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Machine learning

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Journal Article

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21 resources

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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
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
Jacobs, B. (2018). Categorical Aspects of Parameter Learning. ArXiv:1810.05814 [Cs]. Retrieved from http://arxiv.org/abs/1810.05814
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
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
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
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
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
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
Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452–459. https://doi.org/10/gdxwhq
Graves, A., Wayne, G., & Danihelka, I. (2014). Neural Turing Machines. ArXiv:1410.5401 [Cs]. Retrieved from http://arxiv.org/abs/1410.5401
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
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/

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