578 citations · 651 across the 29 of their papers we have counts for
12 papers · 1 filter
Human Joint Kinematics Diffusion-Refinement for Stochastic Motion Prediction
Dong Wei, Huaijiang Sun, Bin Li +4
Stochastic human motion prediction aims to forecast multiple plausible future motions given a single pose sequence from the past. Most previous works focus on designing elaborate l…
Regularized Stein Variational Gradient Flow
Ye He, Krishnakumar Balasubramanian, Bharath K. Sriperumbudur +1
The Stein Variational Gradient Descent (SVGD) algorithm is a deterministic particle method for sampling. However, a mean-field analysis reveals that the gradient flow corresponding…
Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions
Hongrui Chen, Holden Lee, Jianfeng Lu
We give an improved theoretical analysis of score-based generative modeling. Under a score estimate with small error (averaged across timesteps), we provide efficient converg…
Convergence of score-based generative modeling for general data distributions
Holden Lee, Jianfeng Lu, Yixin Tan
Score-based generative modeling (SGM) has grown to be a hugely successful method for learning to generate samples from complex data distributions such as that of images and audio.…
Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective
Tanya Marwah, Zachary C. Lipton, Jianfeng Lu +1
A burgeoning line of research leverages deep neural networks to approximate the solutions to high dimensional PDEs, opening lines of theoretical inquiry focused on explaining how i…
A deep learning framework for geodesics under spherical Wasserstein-Fisher-Rao metric and its application for weighted sample generation
Yang Jing, Jiaheng Chen, Lei Li +1
Wasserstein-Fisher-Rao (WFR) distance is a family of metrics to gauge the discrepancy of two Radon measures, which takes into account both transportation and weight change. Spheric…