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Neural networks can implement arbitrary functions.
ReLU Deep Neural Networks and Linear Finite Elements
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Gradient Descent Happens in a Tiny Subspace
G. Gur-Ari, D. A. Roberts, and E. Dyer · 2018
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Deep, Skinny Neural Networks are not Universal Approximators
J. Johnson · 2018
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Deep neural networks as gaussian processes
J. Lee, J. Sohl-Dickstein, J. Pennington, R. Novak, S. Schoenholz, and Y. Bahri · 2018
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L. Xiao, Y. Bahri, J. Sohl-Dickstein, S. S. Schoenholz, and J. Pennington · 2018
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Continuously Flattening Polyhedra Using Straight Skeletons
Z. Abel, E. D. Demaine, M. L. Demaine, J.-i. Itoh, A. Lubiw, C. Nara, and J. O’Rourke · 2014
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R. Arora, A. Basu, P. Mianjy, and A. Mukherjee · 2016
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S. Carlsson, H. Azizpour, A. S. Razavian, J. Sullivan, and K. Smith · 2017
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UCI machine learning repository, 2017
D. Dua and C. Graff · 2017
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Principles of Riemannian Geometry in Neural Networks
M. Hauser and A. Ray · 2017
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Mixed selectivity morphs population codes in prefrontal cortex
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Tropical geometry of deep neural networks
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On the geometry of rectifier convolutional neural networks
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Empirical studies on the properties of linear regions in deep neural networks
X. Zhang and D. Wu · 2020
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Learning curves for overparametrized deep neural networks: A field theory perspective
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Disentangling deep neural networks with rectified linear units using duality
C. Lakshminarayanan and A. V. Singh · 2021
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Exact priors of finite neural networks
J. A. Zavatone-Veth and C. Pehlevan · 2021
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Decomposing neural networks as mappings of correlation functions
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