1 citations · 2 across the 6 of their papers we have counts for
6 papers
Nonlinear Assimilation via Score-based Sequential Langevin Sampling
Zhao Ding, Chenguang Duan, Yuling Jiao +3
This paper introduces score-based sequential Langevin sampling (SSLS), a novel approach to nonlinear data assimilation within a recursive Bayesian filtering framework. The proposed…
Characteristic Learning for Provable One Step Generation
Zhao Ding, Chenguang Duan, Yuling Jiao +3
We propose the characteristic generator, an one-step generative model that combines the sampling efficiency of generative adversarial networks (GANs) with the training stability of…
Deep conditional distribution learning via conditional Föllmer flow
Jinyuan Chang, Zhao Ding, Yuling Jiao +2
We introduce an ordinary differential equation (ODE) based deep generative method for learning conditional distributions, named Conditional Föllmer Flow. Starting from a standard G…
Semi-Supervised Deep Sobolev Regression: Estimation and Variable Selection by ReQU Neural Network
Zhao Ding, Chenguang Duan, Yuling Jiao +1
We propose SDORE, a Semi-supervised Deep Sobolev Regressor, for the nonparametric estimation of the underlying regression function and its gradient. SDORE employs deep ReQU neural…
Sampling via Föllmer Flow
Zhao Ding, Yuling Jiao, Xiliang Lu +2
We introduce a novel unit-time ordinary differential equation (ODE) flow called the preconditioned Föllmer flow, which efficiently transforms a Gaussian measure into a desired targ…
Robust Decoding from Binary Measurements with Cardinality Constraint Least Squares
Zhao Ding, Junjun Huang, Yuling Jiao +2
The main goal of 1-bit compressive sampling is to decode dimensional signals with sparsity level from binary measurements. This is a challenging task due to the presenc…