Molei Tao | DeepAI

DeepAI AI Chat

Featured Co-authors

Xin Wang
382 publications
Hongyuan Zha
98 publications
Tuo Zhao
94 publications
Georgios Piliouras
78 publications
J. Nathan Kutz
64 publications
Evangelos A. Theodorou
48 publications
Yongxin Chen
45 publications
Minshuo Chen
31 publications
Santosh S. Vempala
31 publications
Yuqing Wang
25 publications
Fei Lu
25 publications

∙ 03/03/2023

Deep Momentum Multi-Marginal Schrödinger Bridge

Reconstructing population dynamics using only samples from distributions...

0 Tianrong Chen, et al. ∙

∙ 09/30/2022

Data-driven discovery of non-Newtonian astronomy via learning non-Euclidean Hamiltonian

Incorporating the Hamiltonian structure of physical dynamics into deep l...

0 Oswin So, et al. ∙

∙ 07/13/2022

NySALT: Nyström-type inference-based schemes adaptive to large time-stepping

Large time-stepping is important for efficient long-time simulations of ...

0 Xingjie Li, et al. ∙

∙ 06/11/2022

gDDIM: Generalized denoising diffusion implicit models

Our goal is to extend the denoising diffusion implicit model (DDIM) to g...

0 Qinsheng Zhang, et al. ∙

∙ 06/08/2022

Alternating Mirror Descent for Constrained Min-Max Games

In this paper we study two-player bilinear zero-sum games with constrain...

0 Andre Wibisono, et al. ∙

∙ 05/27/2022

Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport

The problem of optimization on Stiefel manifold, i.e., minimizing functi...

21 Lingkai Kong, et al. ∙

∙ 10/07/2021

Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect

Recent empirical advances show that training deep models with large lear...

0 Yuqing Wang, et al. ∙

∙ 09/24/2021

The Mirror Langevin Algorithm Converges with Vanishing Bias

The technique of modifying the geometry of a problem from Euclidean to H...

0 Ruilin Li, et al. ∙

∙ 09/08/2021

Sqrt(d) Dimension Dependence of Langevin Monte Carlo

This article considers the popular MCMC method of unadjusted Langevin Mo...

0 Ruilin Li, et al. ∙

∙ 03/09/2021

Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps

We consider the learning and prediction of nonlinear time series generat...

0 Renyi Chen, et al. ∙

∙ 06/16/2020

Hessian-Free High-Resolution Nesterov Acceleration for Sampling

We propose an accelerated-gradient-based MCMC method. It relies on a mod...

0 Ruilin Li, et al. ∙

∙ 06/16/2020

Hessian-Free High-Resolution Nesterov Accelerationfor Sampling

We propose an accelerated-gradient-based MCMC method. It relies on a mod...

0 Ruilin Li, et al. ∙

∙ 02/20/2020

Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients

Common Stochastic Gradient MCMC methods approximate gradients by stochas...

3 Ruilin Li, et al. ∙

∙ 02/14/2020

Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? – A Neural Tangent Kernel Perspective

Deep residual networks (ResNets) have demonstrated better generalization...

9 Kaixuan Huang, et al. ∙

∙ 02/14/2020

Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function

This article suggests that deterministic Gradient Descent, which does no...

13 Lingkai Kong, et al. ∙

∙ 01/27/2020

Variational Optimization on Lie Groups, with Examples of Leading (Generalized) Eigenvalue Problems

The article considers smooth optimization of functions on Lie groups. By...

0 Molei Tao, et al. ∙

∙ 03/25/2019

Dynamic mode decomposition for multiscale nonlinear physics

We present a data-driven method for separating complex, multiscale syste...

0 Daniel Dylewsky, et al. ∙

∙ 03/25/2019

Data-driven multiscale decompositions for forecasting and model discovery

We present a data-driven method for separating complex, multiscale syste...

0 Daniel Dylewsky, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world