research
∙
09/19/2023
Diffusion Methods for Generating Transition Paths
In this work, we seek to simulate rare transitions between metastable st...
share
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
∙
05/23/2023
Understanding Text-driven Motion Synthesis with Keyframe Collaboration via Diffusion Models
The emergence of text-driven motion synthesis technique provides animato...
research
∙
05/06/2023
Accelerate Langevin Sampling with Birth-Death process and Exploration Component
Sampling a probability distribution with known likelihood is a fundament...
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
∙
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
∙
12/04/2019
Universal approximation of symmetric and anti-symmetric functions
We consider universal approximations of symmetric and anti-symmetric fun...
research
∙
11/08/2019
Estimating Normalizing Constants for Log-Concave Distributions: Algorithms and Lower Bounds
Estimating the normalizing constant of an unnormalized probability distr...
research
∙
05/18/2018
