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We study the expressivity of deep neural networks.
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Deep residual learning for image recognition
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Flexible Multi-layer Sparse Approximations of Matrices and Applications
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Solving ill-posed inverse problems using iterative deep neural networks
J. Adler and O. Öktem · 2017
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Deep, deep trouble. deep learning’s impact on image processing, mathematics, and humanity
M. Elad · 2017
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Nonparametric regression using deep neural networks with ReLU activation function
J. Schmidt-Hieber · 2017
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Quantum-chemical insights from deep tensor neural networks
K. T. Schütt, F. Arbabzadah, S. Chmiela, K. R. Müller, and A. Tkatchenko · 2017
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Error bounds for approximations with deep ReLU networks
D. Yarotsky · 2017
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Solving stochastic differential equations and kolmogorov equations by means of deep learning
C. Beck, S. Becker, P. Grohs, N. Jaafari, and A. Jentzen · 2018
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Maß- und Integrationstheorie
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Vampnets: Deep learning of molecular kinetics
A. Mardt, L. Pasquali, H. Wu, and F. Noé · 2018
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Skip connections eliminate singularities
Emin Orhan and Xaq Pitkow · 2018
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Optimal approximation of piecewise smooth functions using deep ReLU neural networks
P. Petersen and F. Voigtlaender · 2018
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Provable approximation properties for deep neural networks
U. Shaham, A. Cloninger, and R. R. Coifman · 2018
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Optimal approximation of continuous functions by very deep ReLU networks
Dmitry Yarotsky · 2018
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Nearly-tight VC-dimension and Pseudodimension Bounds for Piecewise Linear Neural Networks
Peter L Bartlett, Nick Harvey, Christopher Liaw, and Abbas Mehrabian · 2019
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