Jonathan W. Siegel

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Jinchao XuProfessor of Mathematics, Penn State UniversityVerified email at psu.edu
Qingguo HongAssistant Professor, Missouri University of Science and TechnologyVerified email at mst.edu
Wenrui HaoAssociate professor of mathematics, Penn State UniversityVerified email at psu.edu
Adam M. KrajewskiPhD Student, The Pennsylvania State UniversityVerified email at psu.edu
Zi-Kui Liu, Dorothy Pate Enright Profes...The Pennsylvania State UniversityVerified email at psu.edu
Limin MaDepartment of Mathematics, Pennsylvania State UniversityVerified email at psu.edu
Russel CaflischProfessor of Mathematics and Director, Courant Institute, NYUVerified email at courant.nyu.edu
Hung-Hsu ChouMunich Center for Machine Learning (MCML), Technical University of Munich (TUM)Verified email at ucla.edu
Stephan WojtowytschUniversity of PittsburghVerified email at pitt.edu
Jianhong ChenPennsylvania State UniversityVerified email at psu.edu
Pengchuan ZhangMeta AIVerified email at fb.com
Yuwen LiZhejiang UniversityVerified email at zju.edu.cn
Ronald DeVoreProfessor of Matematics, Texas A&M UniversityVerified email at math.tamu.edu
Rahul ParhiÉcole polytechnique fédérale de LausanneVerified email at epfl.ch
Robert D NowakKeith and Jane Morgan Nosbusch Professor in ECE, Grace Wahba Professor of Data ScienceVerified email at wisc.edu
Kanan GuptaTexas A&M UniversityVerified email at tamu.edu
Ömer Faruk TekinGraduate Student, University of California at Los AngelesVerified email at math.ucla.edu
Jason M. KlusowskiAssistant Professor, Department of Operations Research & Financial EngineeringVerified email at princeton.edu

Jonathan W. Siegel

Assistant Professor, Texas A&M University

Verified email at tamu.edu - Homepage

Approximation Theory Statistics Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Approximation rates for neural networks with general activation functions JW Siegel, J Xu Neural Networks 128, 313-321, 2020	156	2020
Sharp bounds on the approximation rates, metric entropy, and n-widths of shallow neural networks JW Siegel, J Xu Foundations of Computational Mathematics, 1-57, 2022	72*	2022
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	62*	2022
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	52*	2023
Accelerated first-order methods: Differential equations and Lyapunov functions JW Siegel arXiv preprint arXiv:1903.05671, 2019	47	2019
Characterization of the variation spaces corresponding to shallow neural networks JW Siegel, J Xu Constructive Approximation, 1-24, 2023	40	2023
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	29	2022
Accelerated optimization with orthogonality constraints JW Siegel arXiv preprint arXiv:1903.05204, 2019	22	2019
Optimal convergence rates for the orthogonal greedy algorithm JW Siegel, J Xu IEEE Transactions on Information Theory 68 (5), 3354-3361, 2022	21*	2022
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	19	2022
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	15*	2023
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	12	2022
Training Sparse Neural Networks using Compressed Sensing JW Siegel, J Chen, P Zhang, J Xu arXiv preprint arXiv:2008.09661, 2020	5	2020
Optimal Approximation of Zonoids and Uniform Approximation by Shallow Neural Networks JW Siegel arXiv preprint arXiv:2307.15285, 2023	4	2023
Entropy-based convergence rates of greedy algorithms Y Li, J Siegel arXiv preprint arXiv:2304.13332, 2023	4	2023
Accelerated First-Order Optimization with Orthogonality Constraints JW Siegel University of California, Los Angeles, 2018	4	2018
Weighted variation spaces and approximation by shallow ReLU networks R DeVore, RD Nowak, R Parhi, JW Siegel arXiv preprint arXiv:2307.15772, 2023	3	2023
Achieving acceleration despite very noisy gradients K Gupta, J Siegel, S Wojtowytsch arXiv preprint arXiv:2302.05515, 2023	3	2023
Accuracy, Efficiency and Optimization of Signal Fragmentation R Caflisch, HH Chou, JW Siegel Multiscale Modeling & Simulation 18 (2), 737-757, 2020	2	2020
Extended Regularized Dual Averaging Methods for Stochastic Optimization JW Siegel, J Xu arXiv preprint arXiv:1904.02316, 2019	2	2019

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