research
∙
09/19/2023
Diffusion Methods for Generating Transition Paths
In this work, we seek to simulate rare transitions between metastable st...
share
research
∙
09/08/2023
Riemannian Langevin Monte Carlo schemes for sampling PSD matrices with fixed rank
This paper introduces two explicit schemes to sample matrices from Gibbs...
research
∙
09/05/2023
NICE 2023 Zero-shot Image Captioning Challenge
In this report, we introduce NICE project[<https://nice.lgresearch.ai/>]...
research
∙
08/01/2023
Benchmarking Ultra-High-Definition Image Reflection Removal
Deep learning based methods have achieved significant success in the tas...
research
∙
07/13/2023
Deep Network Approximation: Beyond ReLU to Diverse Activation Functions
This paper explores the expressive power of deep neural networks for a d...
research
∙
05/29/2023
Quantum variational embedding for ground-state energy problems: sum of squares and cluster selection
We introduce a sum-of-squares SDP hierarchy approximating the ground-sta...
research
∙
05/23/2023
Understanding Text-driven Motion Synthesis with Keyframe Collaboration via Diffusion Models
The emergence of text-driven motion synthesis technique provides animato...
research
∙
05/19/2023
The probability flow ODE is provably fast
We provide the first polynomial-time convergence guarantees for the prob...
research
∙
05/06/2023
Accelerate Langevin Sampling with Birth-Death process and Exploration Component
Sampling a probability distribution with known likelihood is a fundament...
research
∙
04/21/2023
Score-based Transport Modeling for Mean-Field Fokker-Planck Equations
We use the score-based transport modeling method to solve the mean-field...
research
∙
04/18/2023
Convergence of stochastic gradient descent under a local Lajasiewicz condition for deep neural networks
We extend the global convergence result of Chatterjee <cit.> by consider...
research
∙
04/13/2023
Meta-Auxiliary Learning for Adaptive Human Pose Prediction
Predicting high-fidelity future human poses, from a historically observe...
research
∙
04/10/2023
DeFeeNet: Consecutive 3D Human Motion Prediction with Deviation Feedback
Let us rethink the real-world scenarios that require human motion predic...
research
∙
03/12/2023
Global Optimality of Elman-type RNN in the Mean-Field Regime
We analyze Elman-type Recurrent Reural Networks (RNNs) and their trainin...
research
∙
02/11/2023
A Policy Gradient Framework for Stochastic Optimal Control Problems with Global Convergence Guarantee
In this work, we consider the stochastic optimal control problem in cont...
research
∙
01/29/2023
On Enhancing Expressive Power via Compositions of Single Fixed-Size ReLU Network
This paper studies the expressive power of deep neural networks from the...
research
∙
01/24/2023
On the convergence of Sobolev gradient flow for the Gross-Pitaevskii eigenvalue problem
We study the convergences of three projected Sobolev gradient flows to t...
research
∙
12/01/2022
A Structure-guided Effective and Temporal-lag Connectivity Network for Revealing Brain Disorder Mechanisms
Brain network provides important insights for the diagnosis of many brai...
research
∙
11/15/2022
Regularized Stein Variational Gradient Flow
The Stein Variational Gradient Descent (SVGD) algorithm is an determinis...
research
∙
11/03/2022
Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions
In this paper, we focus on the theoretical analysis of diffusion-based g...
research
∙
10/21/2022
Neural Network Approximations of PDEs Beyond Linearity: Representational Perspective
A burgeoning line of research has developed deep neural networks capable...
research
∙
10/19/2022
On Representing Mixed-Integer Linear Programs by Graph Neural Networks
While Mixed-integer linear programming (MILP) is NP-hard in general, pra...
research
∙
10/12/2022
Human Joint Kinematics Diffusion-Refinement for Stochastic Motion Prediction
Stochastic human motion prediction aims to forecast multiple plausible f...
research
∙
09/26/2022
Convergence of score-based generative modeling for general data distributions
Score-based generative modeling (SGM) has grown to be a hugely successfu...
research
∙
09/25/2022
On Representing Linear Programs by Graph Neural Networks
Learning to optimize is a rapidly growing area that aims to solve optimi...
research
∙
08/25/2022
A deep learning framework for geodesics under spherical Wasserstein-Fisher-Rao metric and its application for weighted sample generation
Wasserstein-Fisher-Rao (WFR) distance is a family of metrics to gauge th...
research
∙
08/02/2022
Overlooked Poses Actually Make Sense: Distilling Privileged Knowledge for Human Motion Prediction
Previous works on human motion prediction follow the pattern of building...
research
∙
07/21/2022
One-dimensional Tensor Network Recovery
We study the recovery of the underlying graphs or permutations for tenso...
research
∙
06/13/2022
Convergence for score-based generative modeling with polynomial complexity
Score-based generative modeling (SGM) is a highly successful approach fo...
research
∙
05/31/2022
Vector-wise Joint Diagonalization of Almost Commuting Matrices
This work aims to numerically construct exactly commuting matrices close...
research
∙
05/05/2022
Neural Network Based Variational Methods for Solving Quadratic Porous Medium Equations in High Dimensions
In this paper, we propose and study neural network based methods for sol...
research
∙
05/04/2022
Asymptotic analysis of diabatic surface hopping algorithm in the adiabatic and non-adiabatic limits
Surface hopping algorithms, as an important class of quantum dynamics si...
research
∙
01/31/2022
Single Time-scale Actor-critic Method to Solve the Linear Quadratic Regulator with Convergence Guarantees
We propose a single time-scale actor-critic algorithm to solve the linea...
research
∙
01/25/2022
A Regularity Theory for Static Schrödinger Equations on ℝ^d in Spectral Barron Spaces
Spectral Barron spaces have received considerable interest recently as i...
research
∙
11/25/2021
Low-rank approximation for multiscale PDEs
Historically, analysis for multiscale PDEs is largely unified while nume...
research
∙
10/13/2021
Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality
In this paper, we study the statistical limits of deep learning techniqu...
research
∙
09/03/2021
Optimal artificial boundary conditions based on second-order correctors for three dimensional random elliptic media
We are interested in numerical algorithms for computing the electrical f...
research
∙
06/14/2021
On the Representation of Solutions to Elliptic PDEs in Barron Spaces
Numerical solutions to high-dimensional partial differential equations (...
research
∙
05/04/2021
A Priori Generalization Error Analysis of Two-Layer Neural Networks for Solving High Dimensional Schrödinger Eigenvalue Problems
This paper analyzes the generalization error of two-layer neural network...
research
∙
03/10/2021
Incorporating Orientations into End-to-end Driving Model for Steering Control
In this paper, we present a novel end-to-end deep neural network model f...
research
∙
03/01/2021
Structure-preserving numerical schemes for Lindblad equations
We study a family of structure-preserving deterministic numerical scheme...
research
∙
02/22/2021
Actor-Critic Method for High Dimensional Static Hamilton–Jacobi–Bellman Partial Differential Equations based on Neural Networks
We propose a novel numerical method for high dimensional Hamilton–Jacobi...
research
∙
01/05/2021
A Priori Generalization Analysis of the Deep Ritz Method for Solving High Dimensional Elliptic Equations
This paper concerns the a priori generalization analysis of the Deep Rit...
research
∙
01/05/2021
On the global convergence of randomized coordinate gradient descent for non-convex optimization
In this work, we analyze the global convergence property of coordinate g...
research
∙
12/21/2020
Complexity of zigzag sampling algorithm for strongly log-concave distributions
We study the computational complexity of zigzag sampling algorithm for s...
research
∙
12/15/2020
Neural Collapse with Cross-Entropy Loss
We consider the variational problem of cross-entropy loss with n feature...
research
∙
12/03/2020
SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising
Deep learning (DL) based hyperspectral images (HSIs) denoising approache...
research
∙
11/01/2020
Manifold Learning and Nonlinear Homogenization
We describe an efficient domain decomposition-based framework for nonlin...
research
∙
10/28/2020
Fast localization of eigenfunctions via smoothed potentials
We study the problem of predicting highly localized low-lying eigenfunct...
research
∙
10/22/2020