32 citations · 125 across the 23 of their papers we have counts for
6 papers · 1 filter
Understanding Knowledge Transferability for Transfer Learning: A Survey
Haohua Wang, Jingge Wang, Zijie Zhao +9
Transfer learning has become an essential paradigm in artificial intelligence, enabling the transfer of knowledge from a source task to improve performance on a target task. This a…
Enhancing Continuous Domain Adaptation with Multi-Path Transfer Curriculum
Hanbing Liu, Jingge Wang, Xuan Zhang +2
Addressing the large distribution gap between training and testing data has long been a challenge in machine learning, giving rise to fields such as transfer learning and domain ad…
Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge
Jingge Wang, Liyan Xie, Yao Xie +2
Domain generalization aims at learning a universal model that performs well on unseen target domains, incorporating knowledge from multiple source domains. In this research, we con…
Class-conditioned Domain Generalization via Wasserstein Distributional Robust Optimization
Jingge Wang, Yang Li, Liyan Xie +1
Given multiple source domains, domain generalization aims at learning a universal model that performs well on any unseen but related target domain. In this work, we focus on the do…
SGB: Stochastic Gradient Bound Method for Optimizing Partition Functions
Jing Wang, Anna Choromanska
This paper addresses the problem of optimizing partition functions in a stochastic learning setting. We propose a stochastic variant of the bound majorization algorithm that relies…
Dendrite Net: A White-Box Module for Classification, Regression, and System Identification
Gang Liu, Jing Wang
The simulation of biological dendrite computations is vital for the development of artificial intelligence (AI). This paper presents a basic machine learning algorithm, named Dendr…