| Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator Q Li, F Dietrich, EM Bollt, IG Kevrekidis Chaos: An Interdisciplinary Journal of Nonlinear Science 27 (10), 103111, 2017 | 236 | 2017 |
| Stochastic modified equations and adaptive stochastic gradient algorithms Q Li, C Tai, W E Proceedings of the 34th International Conference on Machine Learning 70 …, 2017 | 186 | 2017 |
| Maximum Principle Based Algorithms for Deep Learning Q Li, L Chen, C Tai, W E Journal of Machine Learning Research 18 (165), 1-29, 2018 | 145 | 2018 |
| Stochastic modified equations and dynamics of stochastic gradient algorithms i: Mathematical foundations Q Li, C Tai, E Weinan The Journal of Machine Learning Research 20 (1), 1474-1520, 2019 | 65 | 2019 |
| A mean-field optimal control formulation of deep learning E Weinan, J Han, Q Li Research in the Mathematical Sciences 6 (1), 1-41, 2019 | 58 | 2019 |
| An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks Q Li, S Hao Proceedings of the 35th International Conference on Machine Learning 80 …, 2018 | 53 | 2018 |
| Deep Learning via Dynamical Systems: An Approximation Perspective Q Li, T Lin, Z Shen arXiv preprint arXiv:1912.10382, 2019 | 43 | 2019 |
| Machine learning enables polymer cloud-point engineering via inverse design JN Kumar, Q Li, KYT Tang, T Buonassisi, AL Gonzalez-Oyarce, J Ye npj Computational Materials 5 (1), 1-6, 2019 | 35 | 2019 |
| Noisy Hegselmann-Krause systems: phase transition and the 2r-conjecture C Wang, Q Li, W E, B Chazelle Journal of Statistical Physics 166 (5), 1209-1225, 2017 | 35 | 2017 |
| A mean-field optimal control formulation of deep learning J Han, Q Li Research in the Mathematical Sciences 6 (1), 1-41, 2019 | 34 | 2019 |
| Two-step machine learning enables optimized nanoparticle synthesis F Mekki-Berrada, Z Ren, T Huang, WK Wong, F Zheng, J Xie, IPS Tian, ... npj Computational Materials 7 (1), 1-10, 2021 | 31 | 2021 |
| Challenges and opportunities of polymer design with machine learning and high throughput experimentation JN Kumar, Q Li, Y Jun MRS Communications 9 (2), 537-544, 2019 | 27 | 2019 |
| A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent Y Cai, Q Li, Z Shen International Conference on Machine Learning, 882-890, 2019 | 27 | 2019 |
| Prediction of interstitial diffusion activation energies of nitrogen, oxygen, boron and carbon in bcc, fcc, and hcp metals using machine learning Y Zeng, Q Li, K Bai Computational Materials Science 144, 232-247, 2018 | 27 | 2018 |
| On Matching, and Even Rectifying, Dynamical Systems through Koopman Operator Eigenfunctions EM Bollt, Q Li, F Dietrich, I Kevrekidis SIAM Journal on Applied Dynamical Systems 17 (2), 1925-1960, 2018 | 24 | 2018 |
| Inverse design of crystals using generalized invertible crystallographic representation Z Ren, J Noh, S Tian, F Oviedo, G Xing, Q Liang, A Aberle, Y Liu, Q Li, ... arXiv preprint arXiv:2005.07609, 2020 | 23 | 2020 |
| Computing committor functions for the study of rare events using deep learning Q Li, B Lin, W Ren The Journal of Chemical Physics 151 (5), 054112, 2019 | 23 | 2019 |
| An emergent space for distributed data with hidden internal order through manifold learning FP Kemeth, SW Haugland, F Dietrich, T Bertalan, K Höhlein, Q Li, ... IEEE Access 6, 77402-77413, 2018 | 23 | 2018 |
| Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics Z Ren, F Oviedo, M Thway, SIP Tian, Y Wang, H Xue, J Dario Perea, ... npj Computational Materials 6 (1), 1-9, 2020 | 21 | 2020 |
| An Emergent Space for Distributed Data With Hidden Internal Order Through Manifold Learning FP Kemeth, SW Haugland, F Dietrich, T Bertalan, K Höhlein, Q Li, ... IEEE Access 6, 77402-77413, 2018 | 21 | 2018 |
Weinan EProfessor of Mathematics, Princeton UniversityVerified email at math.princeton.edu
