| Approximation rates for neural networks with general activation functions JW Siegel, J Xu Neural Networks 128, 313-321, 2020 | 217 | 2020 |
| Sharp Bounds on the Approximation Rates, Metric Entropy, and n-Widths of Shallow Neural Networks JW Siegel, J Xu Foundations of Computational Mathematics 24 (2), 481-537, 2024 | 127* | 2024 |
| Greedy Training Algorithms for Neural Networks and Applications to PDEs JW Siegel, Q Hong, X Jin, W Hao, J Xu Journal of Computational Physics, 112084, 2023 | 116* | 2023 |
| High-order approximation rates for shallow neural networks with cosine and ReLUk activation functions JW Siegel, J Xu Applied and Computational Harmonic Analysis 58, 1-26, 2022 | 114* | 2022 |
| Characterization of the variation spaces corresponding to shallow neural networks JW Siegel, J Xu Constructive Approximation 57 (3), 1109-1132, 2023 | 82 | 2023 |
| Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces JW Siegel Journal of Machine Learning Research 24 (357), 1-52, 2023 | 79* | 2023 |
| Accelerated first-order methods: Differential equations and Lyapunov functions JW Siegel arXiv preprint arXiv:1903.05671, 2019 | 68 | 2019 |
| On the activation function dependence of the spectral bias of neural networks Q Hong, JW Siegel, Q Tan, J Xu arXiv preprint arXiv:2208.04924, 2022 | 52 | 2022 |
| Extensible Structure-informed prediction of formation energy with improved accuracy and usability employing neural networks AM Krajewski, JW Siegel, J Xu, ZK Liu Computational Materials Science 208, 111254, 2022 | 44 | 2022 |
| Optimal convergence rates for the orthogonal greedy algorithm JW Siegel, J Xu IEEE Transactions on Information Theory 68 (5), 3354-3361, 2022 | 41* | 2022 |
| On the expressiveness and spectral bias of KANs Y Wang, JW Siegel, Z Liu, TY Hou arXiv preprint arXiv:2410.01803, 2024 | 38 | 2024 |
| Equivariant Frames and the Impossibility of Continuous Canonicalization N Dym, H Lawrence, JW Siegel arXiv preprint arXiv:2402.16077, 2024 | 37 | 2024 |
| Accelerated optimization with orthogonality constraints JW Siegel arXiv preprint arXiv:1903.05204, 2019 | 27 | 2019 |
| Uniform approximation rates and metric entropy of shallow neural networks L Ma, JW Siegel, J Xu Research in the Mathematical Sciences 9 (3), 46, 2022 | 25 | 2022 |
| Entropy-based convergence rates of greedy algorithms Y Li, JW Siegel Mathematical Models and Methods in Applied Sciences 34 (05), 779-802, 2024 | 16 | 2024 |
| Optimal approximation of zonoids and uniform approximation by shallow neural networks JW Siegel Constructive Approximation, 1-29, 2025 | 14 | 2025 |
| Convergence and error control of consistent PINNs for elliptic PDEs A Bonito, R DeVore, G Petrova, JW Siegel IMA Journal of Numerical Analysis, draf008, 2025 | 14 | 2025 |
| Nesterov acceleration despite very noisy gradients K Gupta, JW Siegel, S Wojtowytsch Advances in Neural Information Processing Systems 37, 20694-20744, 2024 | 13* | 2024 |
| Weighted variation spaces and approximation by shallow ReLU networks R DeVore, RD Nowak, R Parhi, JW Siegel Applied and Computational Harmonic Analysis, 101713, 2024 | 11 | 2024 |
| Training Sparse Neural Networks using Compressed Sensing JW Siegel, J Chen, P Zhang, J Xu arXiv preprint arXiv:2008.09661, 2020 | 9 | 2020 |
Jinchao XuProfessor of Applied Mathematics and Computational Sciences, KAUSTVerified email at kaust.edu.sa
