Accelerated First-Order Optimization with Orthogonality Constraints - Jonathan W. Siegel (2018)

On the Approximation Properties of Neural Networks. - Jonathan W. Siegel; Jinchao Xu (CoRR, 2019)

Training Sparse Neural Networks using Compressed Sensing. - Jonathan W. Siegel; Jianhong Chen; Jinchao Xu (CoRR, 2020)

High-Order Approximation Rates for Neural Networks with ReLUk Activation Functions. - Jonathan W. Siegel; Jinchao Xu (CoRR, 2020)

Accuracy, Efficiency and Optimization of Signal Fragmentation. - Russel Caflisch; Hung-Hsu Chou; Jonathan W. Siegel (Multiscale Model. Simul., 2020)

Approximation rates for neural networks with general activation functions. - Jonathan W. Siegel; Jinchao Xu (Neural Networks, 2020)

Optimal Approximation Rates and Metric Entropy of ReLU$^k$ and Cosine Networks. - Jonathan W. Siegel; Jinchao Xu (CoRR, 2021)

Improved Convergence Rates for the Orthogonal Greedy Algorithm. - Jonathan W. Siegel; Jinchao Xu (CoRR, 2021)

Characterization of the Variation Spaces Corresponding to Shallow Neural Networks. - Jonathan W. Siegel; Jinchao Xu (CoRR, 2021)

An efficient greedy training algorithm for neural networks and applications in PDEs. - Wenrui Hao; Xianlin Jin; Jonathan W. Siegel; Jinchao Xu (CoRR, 2021)

On the Activation Function Dependence of the Spectral Bias of Neural Networks. - Qingguo Hong; Qinyang Tan; Jonathan W. Siegel; Jinchao Xu (CoRR, 2022)

Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev Spaces. - Jonathan W. Siegel (CoRR, 2022)

Optimal Convergence Rates for the Orthogonal Greedy Algorithm. - Jonathan W. Siegel; Jinchao Xu (IEEE Trans. Inf. Theory, 2022)

Sharp Lower Bounds on Interpolation by Deep ReLU Neural Networks at Irregularly Spaced Data. - Jonathan W. Siegel (CoRR, 2023)

Achieving acceleration despite very noisy gradients. - Kanan Gupta; Jonathan W. Siegel; Stephan Wojtowytsch (CoRR, 2023)

Entropy-based convergence rates of greedy algorithms. - Yuwen Li 001; Jonathan W. Siegel (CoRR, 2023)

Optimal Approximation of Zonoids and Uniform Approximation by Shallow Neural Networks. - Jonathan W. Siegel (CoRR, 2023)

Weighted variation spaces and approximation by shallow ReLU networks. - Ronald A. DeVore; Robert D. Nowak; Rahul Parhi; Jonathan W. Siegel (CoRR, 2023)

A qualitative difference between gradient flows of convex functions in finite- and infinite-dimensional Hilbert spaces. - Jonathan W. Siegel; Stephan Wojtowytsch (CoRR, 2023)

Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces. - Jonathan W. Siegel (J. Mach. Learn. Res., 2023)

Greedy training algorithms for neural networks and applications to PDEs. - Jonathan W. Siegel; Qingguo Hong; Xianlin Jin; Wenrui Hao; Jinchao Xu (J. Comput. Phys., 2023)

Equivariant Frames and the Impossibility of Continuous Canonicalization. - Nadav Dym; Hannah Lawrence; Jonathan W. Siegel (ICML, 2024)

Nesterov acceleration despite very noisy gradients. - Kanan Gupta; Jonathan W. Siegel; Stephan Wojtowytsch (NeurIPS, 2024)

Convergence and error control of consistent PINNs for elliptic PDEs. - Andrea Bonito; Ronald A. DeVore; Guergana Petrova; Jonathan W. Siegel (CoRR, 2024)

Approximation Rates for Shallow ReLUk Neural Networks on Sobolev Spaces via the Radon Transform. - Tong Mao; Jonathan W. Siegel; Jinchao Xu (CoRR, 2024)

Sharp lower bounds on the manifold widths of Sobolev and Besov spaces. - Jonathan W. Siegel (J. Complex., 2024)

Sharp Bounds on the Approximation Rates, Metric Entropy, and n-Widths of Shallow Neural Networks. - Jonathan W. Siegel; Jinchao Xu (Found. Comput. Math., 2024)

On the expressiveness and spectral bias of KANs. - Yixuan Wang; Jonathan W. Siegel; Ziming Liu; Thomas Y. Hou 001 (ICLR, 2025)

Optimal Recovery Meets Minimax Estimation. - Ronald A. DeVore; Robert D. Nowak; Rahul Parhi; Guergana Petrova; Jonathan W. Siegel (CoRR, 2025)

Sharp Convergence Rates for Matching Pursuit. - Jason M. Klusowski; Jonathan W. Siegel (IEEE Trans. Inf. Theory, 2025)