Qianxiao Li

SIAM J. Sci. Comput., 2024

Constructing custom thermodynamics using deep learning.

[BibT_eX]

[DOI]

Xiaoli Chen

Beatrice W. Soh

Zi-En Ooi

Eléonore Vissol-Gaudin

Nat. Comput. Sci., 2024

DynGMA: A robust approach for learning stochastic differential equations from data.

[BibT_eX]

[DOI]

Aiqing Zhu

J. Comput. Phys., 2024

Autocorrelation Matters: Understanding the Role of Initialization Schemes for State Space Models.

[BibT_eX]

[DOI]

Fusheng Liu

CoRR, 2024

Learning Macroscopic Dynamics from Partial Microscopic Observations.

[BibT_eX]

[DOI]

Mengyi Chen

CoRR, 2024

Unifying back-propagation and forward-forward algorithms through model predictive control.

[BibT_eX]

[DOI]

Lianhai Ren

CoRR, 2024

Mitigating distribution shift in machine learning-augmented hybrid simulation.

[BibT_eX]

[DOI]

Jiaxi Zhao

CoRR, 2024

StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization.

[BibT_eX]

[DOI]

Shida Wang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

From Generalization Analysis to Optimization Designs for State Space Models.

[BibT_eX]

[DOI]

Fusheng Liu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving.

[BibT_eX]

[DOI]

Sohei Arisaka

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Parameter-Efficient Fine-Tuning with Controls.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Inverse Approximation Theory for Nonlinear Recurrent Neural Networks.

[BibT_eX]

[DOI]

Shida Wang

Zhong Li

Proceedings of the Twelfth International Conference on Learning Representations, 2024

An Optimal Control View of LoRA and Binary Controller Design for Vision Transformers.

[BibT_eX]

[DOI]

Jingpu Cheng

Proceedings of the Computer Vision - ECCV 2024, 2024

2023

An Annealing Mechanism for Adversarial Training Acceleration.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., February, 2023

On stability and regularization for data-driven solution of parabolic inverse source problems.

[BibT_eX]

[DOI]

Mengmeng Zhang

Jijun Liu

J. Comput. Phys., February, 2023

Computing high-dimensional invariant distributions from noisy data.

[BibT_eX]

[DOI]

J. Comput. Phys., February, 2023

Asymptotically Fair Participation in Machine Learning Models: an Optimal Control Perspective.

[BibT_eX]

[DOI]

Zhuotong Chen

Zheng Zhang

CoRR, 2023

Interpolation, Approximation and Controllability of Deep Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2023

Approximation theory of transformer networks for sequence modeling.

[BibT_eX]

[DOI]

CoRR, 2023

A Brief Survey on the Approximation Theory for Sequence Modelling.

[BibT_eX]

[DOI]

CoRR, 2023

Principled Acceleration of Iterative Numerical Methods Using Machine Learning.

[BibT_eX]

[DOI]

Sohei Arisaka

Proceedings of the International Conference on Machine Learning, 2023

2022

From Optimization Dynamics to Generalization Bounds via Łojasiewicz Gradient Inequality.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2022

Personalized Algorithm Generation: A Case Study in Learning ODE Integrators.

[BibT_eX]

[DOI]

SIAM J. Sci. Comput., 2022

Computing the Invariant Distribution of Randomly Perturbed Dynamical Systems Using Deep Learning.

[BibT_eX]

[DOI]

J. Sci. Comput., 2022

Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2022

Self-Healing Robust Neural Networks via Closed-Loop Control.

[BibT_eX]

[DOI]

Zhuotong Chen

Zheng Zhang

J. Mach. Learn. Res., 2022

On the Universal Approximation Property of Deep Fully Convolutional Neural Networks.

[BibT_eX]

[DOI]

Ting Lin

CoRR, 2022

Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization.

[BibT_eX]

[DOI]

CoRR, 2022

Deep Neural Network Approximation of Invariant Functions through Dynamical Systems.

[BibT_eX]

[DOI]

Ting Lin

CoRR, 2022

Transfer Learning for Rapid Extraction of Thickness from Optical Spectra of Semiconductor Thin Films.

[BibT_eX]

[DOI]

Siyu Isaac Parker Tian

Curtis P. Berlinguette

Tonio Buonassisi

Zhe Liu

CoRR, 2022

Accelerating numerical methods by gradient-based meta-solving.

[BibT_eX]

[DOI]

Sohei Arisaka

CoRR, 2022

Connecting Optimization and Generalization via Gradient Flow Path Length.

[BibT_eX]

[DOI]

CoRR, 2022

Adaptive sampling methods for learning dynamical systems.

[BibT_eX]

[DOI]

Zichen Zhao

Proceedings of the Mathematical and Scientific Machine Learning, 2022

Unraveling Model-Agnostic Meta-Learning via The Adaptation Learning Rate.

[BibT_eX]

[DOI]

Yingtian Zou

Fusheng Liu

Proceedings of the Tenth International Conference on Learning Representations, 2022

On the approximation properties of recurrent encoder-decoder architectures.

[BibT_eX]

[DOI]

Zhong Li

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Personalized Algorithm Generation: A Case Study in Meta-Learning ODE Integrators.

[BibT_eX]

[DOI]

CoRR, 2021

Distributed optimization for degenerate loss functions arising from over-parameterization.

[BibT_eX]

[DOI]

Artif. Intell., 2021

A Data Driven Method for Computing Quasipotentials.

[BibT_eX]

[DOI]

Proceedings of the Mathematical and Scientific Machine Learning, 2021

Approximation Theory of Convolutional Architectures for Time Series Modelling.

[BibT_eX]

[DOI]

Zhong Li

Proceedings of the 38th International Conference on Machine Learning, 2021

On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis.

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Learning Representations, 2021

Towards Robust Neural Networks via Close-loop Control.

[BibT_eX]

[DOI]

Zhuotong Chen

Zheng Zhang

Proceedings of the 9th International Conference on Learning Representations, 2021

QROSS: QUBO Relaxation Parameter optimisation via Learning Solver Surrogates.

[BibT_eX]

[DOI]

Tian Huang

Siong Thye Goh

Sabrish Gopalakrishnan

Tao Luo

Hoong Chuin Lau

Proceedings of the 41st IEEE International Conference on Distributed Computing Systems Workshops, 2021

Forward and Inverse Approximation Theory for Linear Temporal Convolutional Networks.

[BibT_eX]

[DOI]

Proceedings of the Geometric Science of Information - 6th International Conference, 2021

Adversarial Invariant Learning.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Amata: An Annealing Mechanism for Adversarial Training Acceleration.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Optimising Stochastic Routing for Taxi Fleets with Model Enhanced Reinforcement Learning.

[BibT_eX]

[DOI]

CoRR, 2020

OnsagerNet: Learning Stable and Interpretable Dynamics using a Generalized Onsager Principle.

[BibT_eX]

[DOI]

CoRR, 2020

Inverse design of crystals using generalized invertible crystallographic representation.

[BibT_eX]

[DOI]

CoRR, 2020

Optimization in Machine Learning: A Distribution Space Approach.

[BibT_eX]

[DOI]

Yongqiang Cai

CoRR, 2020

Collaborative Inference for Efficient Remote Monitoring.

[BibT_eX]

[DOI]

CoRR, 2020

2019

Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations.

[BibT_eX]

[DOI]

Cheng Tai

J. Mach. Learn. Res., 2019

Deep Learning via Dynamical Systems: An Approximation Perspective.

[BibT_eX]

[DOI]

Ting Lin

CoRR, 2019

Computing Committor Functions for the Study of Rare Events Using Deep Learning.

[BibT_eX]

[DOI]

CoRR, 2019

Distributed Optimization for Over-Parameterized Learning.

[BibT_eX]

[DOI]

CoRR, 2019

Decentralized Optimization with Edge Sampling.

[BibT_eX]

[DOI]

Peilin Zhao

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent.

[BibT_eX]

[DOI]

Yongqiang Cai

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

On Matching, and Even Rectifying, Dynamical Systems through Koopman Operator Eigenfunctions.

[BibT_eX]

[DOI]

Erik M. Bollt

Felix Dietrich

Anibal L. Gonzalez-Oyarce

SIAM J. Appl. Dyn. Syst., 2018

Machine learning enables polymer cloud-point engineering via inverse design.

[BibT_eX]

[DOI]

Jun Ye

CoRR, 2018

On the Convergence and Robustness of Batch Normalization.

[BibT_eX]

[DOI]

Yongqiang Cai

CoRR, 2018

Dynamics of Taxi-like Logistics Systems: Theory and Microscopic Simulations.

[BibT_eX]

[DOI]

Bo Yang

CoRR, 2018

A Mean-Field Optimal Control Formulation of Deep Learning.

[BibT_eX]

[DOI]

Jiequn Han

CoRR, 2018

An Emergent Space for Distributed Data With Hidden Internal Order Through Manifold Learning.

[BibT_eX]

[DOI]

IEEE Access, 2018

Turn-by-turn Intelligent Manoeuvring of Driverless Taxis: A Recursive Value Model Enhanced by Reinforcement Learning.

[BibT_eX]

[DOI]

Bo Yang

Proceedings of the 2018 IEEE Intelligent Vehicles Symposium, 2018

An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks.

[BibT_eX]

[DOI]

Shuji Hao

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Maximum Principle Based Algorithms for Deep Learning.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2017

Stochastic Modified Equations and Adaptive Stochastic Gradient Algorithms.

[BibT_eX]

[DOI]

Cheng Tai

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Noisy Hegselmann-Krause systems: Phase transition and the 2R-conjecture.

[BibT_eX]

[DOI]

Proceedings of the 55th IEEE Conference on Decision and Control, 2016

2015

Dynamics of Stochastic Gradient Algorithms.

[BibT_eX]

[DOI]

Cheng Tai