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Qianxiao Li
Qianxiao Li
Assistant Professor, Department of Mathematics, National University of Singapore
Verified email at nus.edu.sg - Homepage
Title
Cited by
Cited by
Year
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
2362017
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
1862017
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
1452018
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
652019
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
582019
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
532018
Deep Learning via Dynamical Systems: An Approximation Perspective
Q Li, T Lin, Z Shen
arXiv preprint arXiv:1912.10382, 2019
432019
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
352019
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
352017
A mean-field optimal control formulation of deep learning
J Han, Q Li
Research in the Mathematical Sciences 6 (1), 1-41, 2019
342019
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
312021
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
272019
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
272019
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
272018
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
242018
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
232020
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
232019
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
232018
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
212020
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
212018
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