| Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool I Daubechies, J Lu, HT Wu Applied and Computational Harmonic Analysis 30 (2), 243-261, 2011 | 2893 | 2011 |
| Solving parametric PDE problems with artificial neural networks Y Khoo, J Lu, L Ying European Journal of Applied Mathematics 32 (3), 421-435, 2021 | 501 | 2021 |
| Deep network approximation for smooth functions J Lu, Z Shen, H Yang, S Zhang SIAM Journal on Mathematical Analysis 53 (5), 5465-5506, 2021 | 392 | 2021 |
| A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions Y Lu, J Lu Advances in Neural Information Processing Systems 33, 2020 | 290 | 2020 |
| Markov state models based on milestoning C Schütte, F Noé, J Lu, M Sarich, E Vanden-Eijnden J. Chem. Phys. 134 (20), 204105, 2011 | 282 | 2011 |
| Improved analysis of score-based generative modeling: User-friendly bounds under minimal smoothness assumptions H Chen, H Lee, J Lu International Conference on Machine Learning, 4735-4763, 2023 | 262 | 2023 |
| Convergence for score-based generative modeling with polynomial complexity H Lee, J Lu, Y Tan Advances in Neural Information Processing Systems 35, 22870-22882, 2022 | 224 | 2022 |
| Convergence of score-based generative modeling for general data distributions H Lee, J Lu, Y Tan International Conference on Algorithmic Learning Theory, 946-985, 2023 | 209 | 2023 |
| Solving for high-dimensional committor functions using artificial neural networks Y Khoo, J Lu, L Ying Research in the Mathematical Sciences 6 (1), 1, 2019 | 177 | 2019 |
| The probability flow ode is provably fast S Chen, S Chewi, H Lee, Y Li, J Lu, A Salim Advances in Neural Information Processing Systems 36, 68552-68575, 2023 | 170 | 2023 |
| Fast construction of hierarchical matrix representation from matrix–vector multiplication L Lin, J Lu, L Ying Journal of Computational Physics 230 (10), 4071-4087, 2011 | 161 | 2011 |
| A priori generalization analysis of the deep Ritz method for solving high dimensional elliptic partial differential equations Y Lu, J Lu, M Wang Conference on learning theory, 3196-3241, 2021 | 153* | 2021 |
| Adaptive local basis set for Kohn-Sham density functional theory in a discontinuous Galerkin framework I: Total energy calculation L Lin, J Lu, L Ying, W E Journal of Computational Physics 231 (4), 2140–2154, 2012 | 152 | 2012 |
| Neural collapse under cross-entropy loss J Lu, S Steinerberger Applied and Computational Harmonic Analysis 59, 224-241, 2022 | 149 | 2022 |
| ELSI: A unified software interface for Kohn–Sham electronic structure solvers VW Yu, F Corsetti, A García, WP Huhn, M Jacquelin, W Jia, B Lange, L Lin, ... Computer Physics Communications 222, 267-285, 2018 | 149 | 2018 |
| Fast algorithm for extracting the diagonal of the inverse matrix with application to the electronic structure analysis of metallic systems L Lin, J Lu, L Ying, R Car, W E Communications in Mathematical Sciences 7 (3), 755-777, 2009 | 141 | 2009 |
| SelInv -- An Algorithm for Selected Inversion of a Sparse Symmetric Matrix L Lin, C Yang, JC Meza, J Lu, L Ying, W E ACM Transactions on Mathematical Software (TOMS) 37 (4), 40, 2011 | 140 | 2011 |
| Discontinuous Hamiltonian Monte Carlo for models with discrete parameters and discontinuous likelihoods A Nishimura, D Dunson, J Lu arXiv preprint arXiv:1705.08510, 2017 | 125* | 2017 |
| Compression of the electron repulsion integral tensor in tensor hypercontraction format with cubic scaling cost J Lu, L Ying Journal of Computational Physics 302, 329-335, 2015 | 120 | 2015 |
| On Explicit -Convergence Rate Estimate for Underdamped Langevin Dynamics Y Cao, J Lu, L Wang Archive for Rational Mechanics and Analysis 247 (5), 90, 2023 | 119 | 2023 |
Lexing YingProfessor of Mathematics, Stanford UniversityVerified email at stanford.edu
