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2020 – today
- 2024
[j17]Aiqing Zhu
, Qianxiao Li:
DynGMA: A robust approach for learning stochastic differential equations from data. J. Comput. Phys. 513: 113200 (2024)- [j16]Xiaoli Chen, Beatrice W. Soh, Zi-En Ooi, Eléonore Vissol-Gaudin, Haijun Yu, Kostya S. Novoselov, Kedar Hippalgaonkar, Qianxiao Li:
Constructing custom thermodynamics using deep learning. Nat. Comput. Sci. 4(1): 66-85 (2024) - [j15]Danimir T. Doncevic, Alexander Mitsos, Yue Guo, Qianxiao Li, Felix Dietrich, Manuel Dahmen, Ioannis G. Kevrekidis:
A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms. SIAM J. Sci. Comput. 46(2): 719- (2024) - [j14]Zhuotong Chen, Zihu Wang, Yifan Yang, Qianxiao Li, Zheng Zhang:
PID Control-Based Self-Healing to Improve the Robustness of Large Language Models. Trans. Mach. Learn. Res. 2024 (2024) - [c24]Chi Zhang, Jingpu Cheng, Qianxiao Li:
An Optimal Control View of LoRA and Binary Controller Design for Vision Transformers. ECCV (53) 2024: 144-160 - [c23]Shida Wang, Zhong Li, Qianxiao Li:
Inverse Approximation Theory for Nonlinear Recurrent Neural Networks. ICLR 2024 - [c22]Chi Zhang, Jingpu Cheng, Yanyu Xu, Qianxiao Li:
Parameter-Efficient Fine-Tuning with Controls. ICML 2024 - [c21]Sohei Arisaka, Qianxiao Li:
Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving. ICML 2024 - [c20]Fusheng Liu, Qianxiao Li:
From Generalization Analysis to Optimization Designs for State Space Models. ICML 2024 - [c19]Shida Wang, Qianxiao Li:
StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization. ICML 2024 - [i47]Jiaxi Zhao, Qianxiao Li:
Mitigating distribution shift in machine learning-augmented hybrid simulation. CoRR abs/2401.09259 (2024) - [i46]Aiqing Zhu, Qianxiao Li:
DynGMA: a robust approach for learning stochastic differential equations from data. CoRR abs/2402.14475 (2024) - [i45]Zhuotong Chen, Zihu Wang, Yifan Yang, Qianxiao Li, Zheng Zhang:
PID Control-Based Self-Healing to Improve the Robustness of Large Language Models. CoRR abs/2404.00828 (2024) - [i44]Fusheng Liu, Qianxiao Li:
From Generalization Analysis to Optimization Designs for State Space Models. CoRR abs/2405.02670 (2024) - [i43]Sohei Arisaka, Qianxiao Li:
Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving. CoRR abs/2405.02952 (2024) - [i42]Lianhai Ren, Qianxiao Li:
Unifying back-propagation and forward-forward algorithms through model predictive control. CoRR abs/2409.19561 (2024) - [i41]Mengyi Chen, Qianxiao Li:
Learning Macroscopic Dynamics from Partial Microscopic Observations. CoRR abs/2410.23938 (2024) - 2023
- [j13]Mengmeng Zhang, Qianxiao Li, Jijun Liu:
On stability and regularization for data-driven solution of parabolic inverse source problems. J. Comput. Phys. 474: 111769 (2023) - [j12]Bo Lin, Qianxiao Li, Weiqing Ren:
Computing high-dimensional invariant distributions from noisy data. J. Comput. Phys. 474: 111783 (2023) - [j11]Nanyang Ye, Qianxiao Li, Xiao-Yun Zhou, Zhanxing Zhu:
An Annealing Mechanism for Adversarial Training Acceleration. IEEE Trans. Neural Networks Learn. Syst. 34(2): 882-893 (2023) - [c18]Sohei Arisaka, Qianxiao Li:
Principled Acceleration of Iterative Numerical Methods Using Machine Learning. ICML 2023: 1041-1059 - [i40]Haotian Jiang, Qianxiao Li, Zhong Li, Shida Wang:
A Brief Survey on the Approximation Theory for Sequence Modelling. CoRR abs/2302.13752 (2023) - [i39]Haotian Jiang, Qianxiao Li:
Approximation theory of transformer networks for sequence modeling. CoRR abs/2305.18475 (2023) - [i38]Haotian Jiang, Qianxiao Li:
Forward and Inverse Approximation Theory for Linear Temporal Convolutional Networks. CoRR abs/2305.18478 (2023) - [i37]Shida Wang, Zhong Li, Qianxiao Li:
Inverse Approximation Theory for Nonlinear Recurrent Neural Networks. CoRR abs/2305.19190 (2023) - [i36]Xiaoli Chen, Beatrice W. Soh, Zi-En Ooi, Eléonore Vissol-Gaudin, Haijun Yu, Kostya S. Novoselov, Kedar Hippalgaonkar, Qianxiao Li:
Constructing Custom Thermodynamics Using Deep Learning. CoRR abs/2308.04119 (2023) - [i35]Jingpu Cheng, Qianxiao Li, Ting Lin, Zuowei Shen:
Interpolation, Approximation and Controllability of Deep Neural Networks. CoRR abs/2309.06015 (2023) - [i34]Zhuotong Chen, Qianxiao Li, Zheng Zhang:
Asymptotically Fair Participation in Machine Learning Models: an Optimal Control Perspective. CoRR abs/2311.10223 (2023) - [i33]Shida Wang, Qianxiao Li:
StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization. CoRR abs/2311.14495 (2023) - 2022
- [j10]Zhong Li, Jiequn Han, Weinan E, Qianxiao Li:
Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks. J. Mach. Learn. Res. 23: 42:1-42:85 (2022) - [j9]Zhuotong Chen, Qianxiao Li, Zheng Zhang:
Self-Healing Robust Neural Networks via Closed-Loop Control. J. Mach. Learn. Res. 23: 319:1-319:54 (2022) - [j8]Bo Lin, Qianxiao Li, Weiqing Ren:
Computing the Invariant Distribution of Randomly Perturbed Dynamical Systems Using Deep Learning. J. Sci. Comput. 91(3): 77 (2022) - [j7]Yue Guo, Felix Dietrich, Tom Bertalan, Danimir T. Doncevic, Manuel Dahmen, Ioannis G. Kevrekidis, Qianxiao Li:
Personalized Algorithm Generation: A Case Study in Learning ODE Integrators. SIAM J. Sci. Comput. 44(4): 1911- (2022) - [j6]Fusheng Liu, Haizhao Yang, Soufiane Hayou, Qianxiao Li:
From Optimization Dynamics to Generalization Bounds via Łojasiewicz Gradient Inequality. Trans. Mach. Learn. Res. 2022 (2022) - [c17]Zhong Li, Haotian Jiang, Qianxiao Li:
On the approximation properties of recurrent encoder-decoder architectures. ICLR 2022 - [c16]Yingtian Zou, Fusheng Liu, Qianxiao Li:
Unraveling Model-Agnostic Meta-Learning via The Adaptation Learning Rate. ICLR 2022 - [c15]Zichen Zhao, Qianxiao Li:
Adaptive sampling methods for learning dynamical systems. MSML 2022: 335-350 - [e1]Bin Dong, Qianxiao Li, Lei Wang, Zhi-Qin John Xu:
Mathematical and Scientific Machine Learning, 15-17 August 2022, Peking University, Beijing, China. Proceedings of Machine Learning Research 190, PMLR 2022 [contents] - [i32]Fusheng Liu, Haizhao Yang, Soufiane Hayou, Qianxiao Li:
Connecting Optimization and Generalization via Gradient Flow Path Length. CoRR abs/2202.10670 (2022) - [i31]Sohei Arisaka, Qianxiao Li:
Accelerating numerical methods by gradient-based meta-solving. CoRR abs/2206.08594 (2022) - [i30]Zhuotong Chen, Qianxiao Li, Zheng Zhang:
Self-Healing Robust Neural Networks via Closed-Loop Control. CoRR abs/2206.12963 (2022) - [i29]Siyu Isaac Parker Tian, Zekun Ren, Selvaraj Venkataraj, Yuanhang Cheng, Daniil Bash, Felipe Oviedo, J. Senthilnath, Vijila Chellappan, Yee-Fun Lim, Armin G. Aberle, Benjamin P. MacLeod, Fraser G. L. Parlane, Curtis P. Berlinguette, Qianxiao Li, Tonio Buonassisi, Zhe Liu:
Transfer Learning for Rapid Extraction of Thickness from Optical Spectra of Semiconductor Thin Films. CoRR abs/2207.02209 (2022) - [i28]Qianxiao Li, Ting Lin, Zuowei Shen:
Deep Neural Network Approximation of Invariant Functions through Dynamical Systems. CoRR abs/2208.08707 (2022) - [i27]Alexander E. Siemenn, Zekun Ren, Qianxiao Li, Tonio Buonassisi:
Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization. CoRR abs/2208.13771 (2022) - [i26]Danimir T. Doncevic, Alexander Mitsos, Yue Guo, Qianxiao Li, Felix Dietrich, Manuel Dahmen, Ioannis G. Kevrekidis:
A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms. CoRR abs/2211.12386 (2022) - [i25]Ting Lin, Zuowei Shen, Qianxiao Li:
On the Universal Approximation Property of Deep Fully Convolutional Neural Networks. CoRR abs/2211.14047 (2022) - 2021
- [j5]Chi Zhang, Qianxiao Li:
Distributed optimization for degenerate loss functions arising from over-parameterization. Artif. Intell. 301: 103575 (2021) - [c14]Nanyang Ye, Qianxiao Li, Xiao-Yun Zhou, Zhanxing Zhu:
Amata: An Annealing Mechanism for Adversarial Training Acceleration. AAAI 2021: 10691-10699 - [c13]Nanyang Ye, Jingxuan Tang, Huayu Deng, Xiao-Yun Zhou, Qianxiao Li, Zhenguo Li, Guang-Zhong Yang, Zhanxing Zhu:
Adversarial Invariant Learning. CVPR 2021: 12446-12454 - [c12]Haotian Jiang, Qianxiao Li:
Forward and Inverse Approximation Theory for Linear Temporal Convolutional Networks. GSI (2) 2021: 342-350 - [c11]Tian Huang, Siong Thye Goh, Sabrish Gopalakrishnan, Tao Luo, Qianxiao Li, Hoong Chuin Lau:
QROSS: QUBO Relaxation Parameter optimisation via Learning Solver Surrogates. ICDCS Workshops 2021: 35-40 - [c10]Zhuotong Chen, Qianxiao Li, Zheng Zhang:
Towards Robust Neural Networks via Close-loop Control. ICLR 2021 - [c9]Zhong Li, Jiequn Han, Weinan E, Qianxiao Li:
On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis. ICLR 2021 - [c8]Haotian Jiang, Zhong Li, Qianxiao Li:
Approximation Theory of Convolutional Architectures for Time Series Modelling. ICML 2021: 4961-4970 - [c7]Bo Lin, Qianxiao Li, Weiqing Ren:
A Data Driven Method for Computing Quasipotentials. MSML 2021: 652-670 - [i24]Zhuotong Chen, Qianxiao Li, Zheng Zhang:
Towards Robust Neural Networks via Close-loop Control. CoRR abs/2102.01862 (2021) - [i23]Tian Huang, Siong Thye Goh, Sabrish Gopalakrishnan, Tao Luo, Qianxiao Li, Hoong Chuin Lau:
QROSS: QUBO Relaxation Parameter Optimisation via Learning Solver Surrogates. CoRR abs/2103.10695 (2021) - [i22]Yue Guo, Felix Dietrich, Tom Bertalan, Danimir T. Doncevic, Manuel Dahmen, Ioannis G. Kevrekidis, Qianxiao Li:
Personalized Algorithm Generation: A Case Study in Meta-Learning ODE Integrators. CoRR abs/2105.01303 (2021) - [i21]Haotian Jiang, Zhong Li, Qianxiao Li:
Approximation Theory of Convolutional Architectures for Time Series Modelling. CoRR abs/2107.09355 (2021) - [i20]Bo Lin, Qianxiao Li, Weiqing Ren:
Computing the Invariant Distribution of Randomly Perturbed Dynamical Systems Using Deep Learning. CoRR abs/2110.11538 (2021) - 2020
- [i19]Chi Zhang, Yong Sheng Soh, Ling Feng, Tianyi Zhou, Qianxiao Li:
Collaborative Inference for Efficient Remote Monitoring. CoRR abs/2002.04759 (2020) - [i18]Yongqiang Cai, Qianxiao Li, Zuowei Shen:
Optimization in Machine Learning: A Distribution Space Approach. CoRR abs/2004.08620 (2020) - [i17]Zekun Ren, Juhwan Noh, Siyu I. P. Tian, Felipe Oviedo, Guangzong Xing, Qiaohao Liang, Armin Aberle, Yi Liu, Qianxiao Li, Senthilnath Jayavelu, Kedar Hippalgaonkar, Yousung Jung, Tonio Buonassisi:
Inverse design of crystals using generalized invertible crystallographic representation. CoRR abs/2005.07609 (2020) - [i16]Haijun Yu, Xinyuan Tian, Weinan E, Qianxiao Li:
OnsagerNet: Learning Stable and Interpretable Dynamics using a Generalized Onsager Principle. CoRR abs/2009.02327 (2020) - [i15]Zhong Li, Jiequn Han, Weinan E, Qianxiao Li:
On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis. CoRR abs/2009.07799 (2020) - [i14]Shen Ren, Qianxiao Li, Liye Zhang, Zheng Qin, Bo Yang:
Optimising Stochastic Routing for Taxi Fleets with Model Enhanced Reinforcement Learning. CoRR abs/2010.11738 (2020) - [i13]Nanyang Ye, Qianxiao Li, Xiao-Yun Zhou, Zhanxing Zhu:
Amata: An Annealing Mechanism for Adversarial Training Acceleration. CoRR abs/2012.08112 (2020) - [i12]Bo Lin, Qianxiao Li, Weiqing Ren:
A Data Driven Method for Computing Quasipotentials. CoRR abs/2012.09111 (2020)
2010 – 2019
- 2019
- [j4]Qianxiao Li, Cheng Tai, Weinan E:
Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations. J. Mach. Learn. Res. 20: 40:1-40:47 (2019) - [c6]Yongqiang Cai, Qianxiao Li, Zuowei Shen:
A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent. ICML 2019: 882-890 - [c5]Chi Zhang, Qianxiao Li, Peilin Zhao:
Decentralized Optimization with Edge Sampling. IJCAI 2019: 658-664 - [i11]Chi Zhang, Qianxiao Li:
Distributed Optimization for Over-Parameterized Learning. CoRR abs/1906.06205 (2019) - [i10]Qianxiao Li, Bo Lin, Weiqing Ren:
Computing Committor Functions for the Study of Rare Events Using Deep Learning. CoRR abs/1906.06285 (2019) - [i9]Qianxiao Li, Ting Lin, Zuowei Shen:
Deep Learning via Dynamical Systems: An Approximation Perspective. CoRR abs/1912.10382 (2019) - 2018
- [j3]Felix P. Kemeth, Sindre W. Haugland, Felix Dietrich, Tom Bertalan, Kevin Höhlein, Qianxiao Li, Erik M. Bollt, Ronen Talmon, Katharina Krischer, Ioannis G. Kevrekidis:
An Emergent Space for Distributed Data With Hidden Internal Order Through Manifold Learning. IEEE Access 6: 77402-77413 (2018) - [j2]Erik M. Bollt, Qianxiao Li, Felix Dietrich, Ioannis G. Kevrekidis:
On Matching, and Even Rectifying, Dynamical Systems through Koopman Operator Eigenfunctions. SIAM J. Appl. Dyn. Syst. 17(2): 1925-1960 (2018) - [c4]Qianxiao Li, Shuji Hao:
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks. ICML 2018: 2991-3000 - [c3]Bo Yang, Qianxiao Li:
Turn-by-turn Intelligent Manoeuvring of Driverless Taxis: A Recursive Value Model Enhanced by Reinforcement Learning. Intelligent Vehicles Symposium 2018: 1659-1664 - [i8]Qianxiao Li, Shuji Hao:
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks. CoRR abs/1803.01299 (2018) - [i7]Weinan E, Jiequn Han, Qianxiao Li:
A Mean-Field Optimal Control Formulation of Deep Learning. CoRR abs/1807.01083 (2018) - [i6]Bo Yang, Qianxiao Li:
Dynamics of Taxi-like Logistics Systems: Theory and Microscopic Simulations. CoRR abs/1807.03488 (2018) - [i5]Yongqiang Cai, Qianxiao Li, Zuowei Shen:
On the Convergence and Robustness of Batch Normalization. CoRR abs/1810.00122 (2018) - [i4]Qianxiao Li, Cheng Tai, Weinan E:
Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations. CoRR abs/1811.01558 (2018) - [i3]Jatin Nitin Kumar, Qianxiao Li, Karen Y. T. Tang, Tonio Buonassisi, Anibal L. Gonzalez-Oyarce, Jun Ye:
Machine learning enables polymer cloud-point engineering via inverse design. CoRR abs/1812.11212 (2018) - 2017
- [j1]Qianxiao Li, Long Chen, Cheng Tai, Weinan E:
Maximum Principle Based Algorithms for Deep Learning. J. Mach. Learn. Res. 18: 165:1-165:29 (2017) - [c2]Qianxiao Li, Cheng Tai, Weinan E:
Stochastic Modified Equations and Adaptive Stochastic Gradient Algorithms. ICML 2017: 2101-2110 - [i2]Qianxiao Li, Long Chen, Cheng Tai, Weinan E:
Maximum Principle Based Algorithms for Deep Learning. CoRR abs/1710.09513 (2017) - 2016
- [c1]Chu Wang, Qianxiao Li, Weinan E, Bernard Chazelle:
Noisy Hegselmann-Krause systems: Phase transition and the 2R-conjecture. CDC 2016: 2632-2637 - 2015
- [i1]Qianxiao Li, Cheng Tai, Weinan E:
Dynamics of Stochastic Gradient Algorithms. CoRR abs/1511.06251 (2015)
Coauthor Index
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last updated on 2024-12-11 20:45 CET by the dblp team
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