Tao Luo

Orcid: 0000-0002-2029-0362

Affiliations:

Shanghai Jiao Tong University, China
Hong Kong University of Science and Technology, Department of Mathematics, Hong Kong (former)

According to our database¹, Tao Luo authored at least 33 papers between 2016 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

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Bibliography

2024

A priori Estimates for Deep Residual Network in Continuous-time Reinforcement Learning.

[BibT_eX]

[DOI]

CoRR, 2024

2023

On Residual Minimization for PDEs: Failure of PINN, Modified Equation, and Implicit Bias.

[BibT_eX]

[DOI]

Tao Luo

Qixuan Zhou

CoRR, 2023

Structure and Gradient Dynamics Near Global Minima of Two-layer Neural Networks.

[BibT_eX]

[DOI]

Leyang Zhang

Yaoyu Zhang

Tao Luo

CoRR, 2023

Optimistic Estimate Uncovers the Potential of Nonlinear Models.

[BibT_eX]

[DOI]

CoRR, 2023

Stochastic Modified Equations and Dynamics of Dropout Algorithm.

[BibT_eX]

[DOI]

CoRR, 2023

Phase Diagram of Initial Condensation for Two-layer Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2023

2022

On the Exact Computation of Linear Frequency Principle Dynamics and Its Generalization.

[BibT_eX]

[DOI]

SIAM J. Math. Data Sci., 2022

Nonlinear Weighted Directed Acyclic Graph and A Priori Estimates for Neural Networks.

[BibT_eX]

[DOI]

Yuqing Li

Tao Luo

Chao Ma

SIAM J. Math. Data Sci., 2022

A regularised deep matrix factorised model of matrix completion for image restoration.

[BibT_eX]

[DOI]

IET Image Process., 2022

Linear Stability Hypothesis and Rank Stratification for Nonlinear Models.

[BibT_eX]

[DOI]

CoRR, 2022

Embedding Principle in Depth for the Loss Landscape Analysis of Deep Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2022

An Experimental Comparison Between Temporal Difference and Residual Gradient with Neural Network Approximation.

[BibT_eX]

[DOI]

CoRR, 2022

Limitation of characterizing implicit regularization by data-independent functions.

[BibT_eX]

[DOI]

CoRR, 2022

Overview frequency principle/spectral bias in deep learning.

[BibT_eX]

[DOI]

Zhi-Qin John Xu

Yaoyu Zhang

Tao Luo

CoRR, 2022

Towards Understanding the Condensation of Neural Networks at Initial Training.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

An Upper Limit of Decaying Rate with Respect to Frequency in Linear Frequency Principle Model.

[BibT_eX]

[DOI]

Proceedings of the Mathematical and Scientific Machine Learning, 2022

2021

Energy Scaling and Asymptotic Properties of One-Dimensional Discrete System with Generalized Lennard-Jones (m, n) Interaction.

[BibT_eX]

[DOI]

Tao Luo

Yang Xiang

Nung Kwan Yip

J. Nonlinear Sci., 2021

Phase Diagram for Two-layer ReLU Neural Networks at Infinite-width Limit.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2021

Embedding Principle: a hierarchical structure of loss landscape of deep neural networks.

[BibT_eX]

[DOI]

CoRR, 2021

MOD-Net: A Machine Learning Approach via Model-Operator-Data Network for Solving PDEs.

[BibT_eX]

[DOI]

CoRR, 2021

Towards Understanding the Condensation of Two-layer Neural Networks at Initial Training.

[BibT_eX]

[DOI]

CoRR, 2021

An Upper Limit of Decaying Rate with Respect to Frequency in Deep Neural Network.

[BibT_eX]

[DOI]

CoRR, 2021

Linear Frequency Principle Model to Understand the Absence of Overfitting in Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2021

Embedding Principle of Loss Landscape of Deep Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Fourier-domain Variational Formulation and Its Well-posedness for Supervised Learning.

[BibT_eX]

[DOI]

CoRR, 2020

A regularized deep matrix factorized model of matrix completion for image restoration.

[BibT_eX]

[DOI]

CoRR, 2020

Towards an Understanding of Residual Networks Using Neural Tangent Hierarchy (NTH).

[BibT_eX]

[DOI]

Yuqing Li

Tao Luo

Nung Kwan Yip

CoRR, 2020

A type of generalization error induced by initialization in deep neural networks.

[BibT_eX]

[DOI]

Proceedings of Mathematical and Scientific Machine Learning, 2020

2019

Theory of the Frequency Principle for General Deep Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2019

Explicitizing an Implicit Bias of the Frequency Principle in Two-layer Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2019

Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2019

2016

Energy Scaling and Asymptotic Properties of Step Bunching in Epitaxial Growth with Elasticity Effects.

[BibT_eX]

[DOI]

Tao Luo

Yang Xiang

Nung Kwan Yip

Multiscale Model. Simul., 2016

Tao Luo

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