default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDJonathan W. Siegel
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2024
[j7]Jonathan W. Siegel, Jinchao Xu:
Sharp Bounds on the Approximation Rates, Metric Entropy, and n-Widths of Shallow Neural Networks. Found. Comput. Math. 24(2): 481-537 (2024)- [j6]Jonathan W. Siegel:
Sharp lower bounds on the manifold widths of Sobolev and Besov spaces. J. Complex. 85: 101884 (2024) - [c1]Nadav Dym, Hannah Lawrence, Jonathan W. Siegel:
Equivariant Frames and the Impossibility of Continuous Canonicalization. ICML 2024 - [i21]Jonathan W. Siegel:
Sharp Lower Bounds on the Manifold Widths of Sobolev and Besov Spaces. CoRR abs/2402.04407 (2024) - [i20]Nadav Dym, Hannah Lawrence, Jonathan W. Siegel:
Equivariant Frames and the Impossibility of Continuous Canonicalization. CoRR abs/2402.16077 (2024) - [i19]Andrea Bonito, Ronald A. DeVore, Guergana Petrova, Jonathan W. Siegel:
Convergence and error control of consistent PINNs for elliptic PDEs. CoRR abs/2406.09217 (2024) - [i18]Tong Mao, Jonathan W. Siegel, Jinchao Xu:
Approximation Rates for Shallow ReLUk Neural Networks on Sobolev Spaces via the Radon Transform. CoRR abs/2408.10996 (2024) - [i17]Yixuan Wang, Jonathan W. Siegel, Ziming Liu, Thomas Y. Hou:
On the expressiveness and spectral bias of KANs. CoRR abs/2410.01803 (2024) - 2023
- [j5]Jonathan W. Siegel
, Qingguo Hong, Xianlin Jin, Wenrui Hao, Jinchao Xu:
Greedy training algorithms for neural networks and applications to PDEs. J. Comput. Phys. 484: 112084 (2023) - [j4]Jonathan W. Siegel:
Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces. J. Mach. Learn. Res. 24: 357:1-357:52 (2023) - [i16]Jonathan W. Siegel:
Sharp Lower Bounds on Interpolation by Deep ReLU Neural Networks at Irregularly Spaced Data. CoRR abs/2302.00834 (2023) - [i15]Kanan Gupta, Jonathan W. Siegel, Stephan Wojtowytsch:
Achieving acceleration despite very noisy gradients. CoRR abs/2302.05515 (2023) - [i14]Yuwen Li, Jonathan W. Siegel:
Entropy-based convergence rates of greedy algorithms. CoRR abs/2304.13332 (2023) - [i13]Jason M. Klusowski, Jonathan W. Siegel:
Sharp Convergence Rates for Matching Pursuit. CoRR abs/2307.07679 (2023) - [i12]Jonathan W. Siegel:
Optimal Approximation of Zonoids and Uniform Approximation by Shallow Neural Networks. CoRR abs/2307.15285 (2023) - [i11]Ronald A. DeVore, Robert D. Nowak, Rahul Parhi, Jonathan W. Siegel:
Weighted variation spaces and approximation by shallow ReLU networks. CoRR abs/2307.15772 (2023) - [i10]Jonathan W. Siegel, Stephan Wojtowytsch:
A qualitative difference between gradient flows of convex functions in finite- and infinite-dimensional Hilbert spaces. CoRR abs/2310.17610 (2023) - 2022
- [j3]Jonathan W. Siegel, Jinchao Xu:
Optimal Convergence Rates for the Orthogonal Greedy Algorithm. IEEE Trans. Inf. Theory 68(5): 3354-3361 (2022) - [i9]Qingguo Hong, Qinyang Tan, Jonathan W. Siegel, Jinchao Xu:
On the Activation Function Dependence of the Spectral Bias of Neural Networks. CoRR abs/2208.04924 (2022) - [i8]Jonathan W. Siegel:
Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev Spaces. CoRR abs/2211.14400 (2022) - 2021
- [i7]Jonathan W. Siegel, Jinchao Xu:
Optimal Approximation Rates and Metric Entropy of ReLU$^k$ and Cosine Networks. CoRR abs/2101.12365 (2021) - [i6]Jonathan W. Siegel, Jinchao Xu:
Improved Convergence Rates for the Orthogonal Greedy Algorithm. CoRR abs/2106.15000 (2021) - [i5]Jonathan W. Siegel, Jinchao Xu:
Characterization of the Variation Spaces Corresponding to Shallow Neural Networks. CoRR abs/2106.15002 (2021) - [i4]Wenrui Hao, Xianlin Jin, Jonathan W. Siegel, Jinchao Xu:
An efficient greedy training algorithm for neural networks and applications in PDEs. CoRR abs/2107.04466 (2021) - 2020
- [j2]Russel Caflisch, Hung Hsu Chou, Jonathan W. Siegel:
Accuracy, Efficiency and Optimization of Signal Fragmentation. Multiscale Model. Simul. 18(2): 737-757 (2020) - [j1]Jonathan W. Siegel, Jinchao Xu:
Approximation rates for neural networks with general activation functions. Neural Networks 128: 313-321 (2020) - [i3]Jonathan W. Siegel, Jianhong Chen, Jinchao Xu:
Training Sparse Neural Networks using Compressed Sensing. CoRR abs/2008.09661 (2020) - [i2]Jonathan W. Siegel, Jinchao Xu:
High-Order Approximation Rates for Neural Networks with ReLUk Activation Functions. CoRR abs/2012.07205 (2020)
2010 – 2019
- 2019
- [i1]Jonathan W. Siegel, Jinchao Xu:
On the Approximation Properties of Neural Networks. CoRR abs/1904.02311 (2019) - 2018
- [b1]Jonathan W. Siegel:
Accelerated First-Order Optimization with Orthogonality Constraints. University of California, Los Angeles, USA, 2018
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-11 22:25 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
