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MACHINE LEARNING
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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

MartinMaroto, 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

Murfet, D. (n.d.). Algebra and Artiﬁcial Intelligence.

Murfet, D. (2019). dmurfet/2simplicialtransformer. Retrieved from https://github.com/dmurfet/2simplicialtransformer (Original work published 2019)

Murfet, D. (2018). dmurfet/deeplinearlogic. Retrieved from https://github.com/dmurfet/deeplinearlogic (Original work published 2016)

Murfet, D. (2018). dmurfet/polysemantics. Retrieved from https://github.com/dmurfet/polysemantics (Original work published 2016)

Murfet, D. (n.d.). Mathematics of AlphaGo.

Murfet, D., & Clift, J. (2019). Derivatives of Turing machines in Linear Logic. ArXiv:1805.11813 [Math]. Retrieved from http://arxiv.org/abs/1805.11813

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

Murfet, D., & Hu, H. (n.d.). Linear logic and deep learning.

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

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

Philipona, D., O’Regan, J., & Nadal, J.P. (2003). Is There Something Out There? Inferring Space from Sensorimotor Dependencies. Neural Computation, 15, 2029–2049. https://doi.org/10/frg7gs

Poggio, T. (2013). Tomaso A. Poggio autobiography (p. 54). Retrieved from http://poggiolab.mit.edu/sites/default/files/cv/tomasopoggio.pdf

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

Siegelmann, H. T., & Sontag, E. D. (1995). On the Computational Power of Neural Nets. Journal of Computer and System Sciences, 50(1), 132–150. https://doi.org/10/dvwtc3

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
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