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Papers
2012.05451
Cited By

The Representation Power of Neural Networks: Breaking the Curse of
Dimensionality

v1v2v3 (latest)

The Representation Power of Neural Networks: Breaking the Curse of Dimensionality

10 December 2020

Moise Blanchard

ArXiv (abs)PDF HTML

Papers citing "The Representation Power of Neural Networks: Breaking the Curse of Dimensionality"

4 / 4 papers shown

Learning smooth functions in high dimensions: from sparse polynomials to
deep neural networks

Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks

Simone Brugiapaglia

210

8

0

04 Apr 2024

Learning Sparsity-Promoting Regularizers using Bilevel Optimization

Learning Sparsity-Promoting Regularizers using Bilevel OptimizationSIAM Journal of Imaging Sciences (SIAM J. Imaging Sci.), 2022

Michael T. McCann

Madeline Mitchell

235

5

0

18 Jul 2022

Lyapunov-Net: A Deep Neural Network Architecture for Lyapunov Function
Approximation

Lyapunov-Net: A Deep Neural Network Architecture for Lyapunov Function ApproximationIEEE Conference on Decision and Control (CDC), 2021

282

54

0

27 Sep 2021

On the approximation of functions by tanh neural networks

On the approximation of functions by tanh neural networksNeural Networks (NN), 2021

Siddhartha Mishra

269

172

0

18 Apr 2021