2 citations · 6 across the 17 of their papers we have counts for
8 papers · 1 filter
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…
Deep Transfer Learning: Model Framework and Error Analysis
Yuling Jiao, Huazhen Lin, Yuchen Luo +1
This paper presents a framework for deep transfer learning, which aims to leverage information from multi-domain upstream data with a large number of samples to a single-domain…
Adv-SSL: Adversarial Self-Supervised Representation Learning with Theoretical Guarantees
Chenguang Duan, Yuling Jiao, Huazhen Lin +2
Learning transferable data representations from abundant unlabeled data remains a central challenge in machine learning. Although numerous self-supervised learning methods have bee…
DRM Revisited: A Complete Error Analysis
Yuling Jiao, Ruoxuan Li, Peiying Wu +2
In this work, we address a foundational question in the theoretical analysis of the Deep Ritz Method (DRM) under the over-parameteriztion regime: Given a target precision level, ho…
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…
A Stabilized Physics Informed Neural Networks Method for Wave Equations
Yuling Jiao, Yuhui Liu, Jerry Zhijian Yang +1
In this article, we propose a novel Stabilized Physics Informed Neural Networks method (SPINNs) for solving wave equations. In general, this method not only demonstrates theoretica…
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…