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Jonathan W. Siegel

Orcid: 0000-0002-1493-4889

According to our database1, Jonathan W. Siegel authored at least 22 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Online presence:

On csauthors.net:

Bibliography

2024
Equivariant Frames and the Impossibility of Continuous Canonicalization.
[BibTeX]
[DOI]
Nadav Dym
,
Hannah Lawrence
,
Jonathan W. Siegel
CoRR, 2024

Sharp Lower Bounds on the Manifold Widths of Sobolev and Besov Spaces.
[BibTeX]
[DOI]
Jonathan W. Siegel
CoRR, 2024

2023
Greedy training algorithms for neural networks and applications to PDEs.
[BibTeX]
[DOI]
Jonathan W. Siegel
,
Qingguo Hong
,
Xianlin Jin
,
Wenrui Hao
,
Jinchao Xu
J. Comput. Phys., July, 2023

A qualitative difference between gradient flows of convex functions in finite- and infinite-dimensional Hilbert spaces.
[BibTeX]
[DOI]
Jonathan W. Siegel
,
Stephan Wojtowytsch
CoRR, 2023

Weighted variation spaces and approximation by shallow ReLU networks.
[BibTeX]
[DOI]
Ronald DeVore
,
Robert D. Nowak
,
Rahul Parhi
,
Jonathan W. Siegel
CoRR, 2023

Optimal Approximation of Zonoids and Uniform Approximation by Shallow Neural Networks.
[BibTeX]
[DOI]
Jonathan W. Siegel
CoRR, 2023

Sharp Convergence Rates for Matching Pursuit.
[BibTeX]
[DOI]
Jason M. Klusowski
,
Jonathan W. Siegel
CoRR, 2023

Entropy-based convergence rates of greedy algorithms.
[BibTeX]
[DOI]
Yuwen Li
,
Jonathan W. Siegel
CoRR, 2023

Achieving acceleration despite very noisy gradients.
[BibTeX]
[DOI]
Kanan Gupta
,
Jonathan W. Siegel
,
Stephan Wojtowytsch
CoRR, 2023

Sharp Lower Bounds on Interpolation by Deep ReLU Neural Networks at Irregularly Spaced Data.
[BibTeX]
[DOI]
Jonathan W. Siegel
CoRR, 2023

2022
Optimal Convergence Rates for the Orthogonal Greedy Algorithm.
[BibTeX]
[DOI]
Jonathan W. Siegel
,
Jinchao Xu
IEEE Trans. Inf. Theory, 2022

Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev Spaces.
[BibTeX]
[DOI]
Jonathan W. Siegel
CoRR, 2022

On the Activation Function Dependence of the Spectral Bias of Neural Networks.
[BibTeX]
[DOI]
Qingguo Hong
,
Qinyang Tan
,
Jonathan W. Siegel
,
Jinchao Xu
CoRR, 2022

2021
An efficient greedy training algorithm for neural networks and applications in PDEs.
[BibTeX]
[DOI]
Wenrui Hao
,
Xianlin Jin
,
Jonathan W. Siegel
,
Jinchao Xu
CoRR, 2021

Characterization of the Variation Spaces Corresponding to Shallow Neural Networks.
[BibTeX]
[DOI]
Jonathan W. Siegel
,
Jinchao Xu
CoRR, 2021

Improved Convergence Rates for the Orthogonal Greedy Algorithm.
[BibTeX]
[DOI]
Jonathan W. Siegel
,
Jinchao Xu
CoRR, 2021

Optimal Approximation Rates and Metric Entropy of ReLU$^k$ and Cosine Networks.
[BibTeX]
[DOI]
Jonathan W. Siegel
,
Jinchao Xu
CoRR, 2021

2020
Approximation rates for neural networks with general activation functions.
[BibTeX]
[DOI]
Jonathan W. Siegel
,
Jinchao Xu
Neural Networks, 2020

Accuracy, Efficiency and Optimization of Signal Fragmentation.
[BibTeX]
[DOI]
Russel Caflisch
,
Hung Hsu Chou
,
Jonathan W. Siegel
Multiscale Model. Simul., 2020

High-Order Approximation Rates for Neural Networks with ReLU<sup>k</sup> Activation Functions.
[BibTeX]
[DOI]
Jonathan W. Siegel
,
Jinchao Xu
CoRR, 2020

Training Sparse Neural Networks using Compressed Sensing.
[BibTeX]
[DOI]
Jonathan W. Siegel
,
Jianhong Chen
,
Jinchao Xu
CoRR, 2020

2019
On the Approximation Properties of Neural Networks.
[BibTeX]
[DOI]
Jonathan W. Siegel
,
Jinchao Xu
CoRR, 2019


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