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We show that any smooth bi-Lipschitz $h$ can be represented exactly as a composition $h_m \circ ...
Approximation Capabilities of Multilayer Feedforward Networks
K. Hornik · 1991
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For neural networks, function determines form
Francesca Albertini and Eduardo D Sontag · 1992
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Reconstructing a neural net from its output
Charles Fefferman · 1994
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Efficient agnostic learning of neural networks with bounded fan-in
Wee Sun Lee, Peter L Bartlett, and Robert C Williamson · 1996
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Convex neural networks
Yoshua Bengio, Nicolas L Roux, Pascal Vincent, Olivier Delalleau, and Patrice Marcotte · 2006
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Predicting parameters in deep learning
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Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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The reversible residual network: Backpropagation without storing activations
Aidan N. Gomez, Mengye Ren, Raquel Urtasun, and Roger B. Grosse · 2017
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Identity matters in deep learning
M. Hardt and T. Ma · 2017
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Peter L Bartlett, David P Helmbold, and Philip M Long · 2018
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Hrushikesh Mhaskar, Qianli Liao, and Tomaso Poggio · 2016
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