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ORCIDJonathan W. Siegel
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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 - [i20]Jonathan W. Siegel:
Sharp Lower Bounds on the Manifold Widths of Sobolev and Besov Spaces. CoRR abs/2402.04407 (2024) - [i19]Nadav Dym, Hannah Lawrence, Jonathan W. Siegel:
Equivariant Frames and the Impossibility of Continuous Canonicalization. CoRR abs/2402.16077 (2024) - [i18]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) - [i17]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) - 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
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