13 citations · 28 across the 7 of their papers we have counts for
7 papers
Online Test-Time Adaptation of Spatial-Temporal Traffic Flow Forecasting
Pengxin Guo, Pengrong Jin, Ziyue Li +2
Accurate spatial-temporal traffic flow forecasting is crucial in aiding traffic managers in implementing control measures and assisting drivers in selecting optimal travel routes.…
A Unified Framework for Unsupervised Domain Adaptation based on Instance Weighting
Jinjing Zhu, Feiyang Ye, Qiao Xiao +3
Despite the progress made in domain adaptation, solving Unsupervised Domain Adaptation (UDA) problems with a general method under complex conditions caused by label shifts between…
Domain Adaptation via Bidirectional Cross-Attention Transformer
Xiyu Wang, Pengxin Guo, Yu Zhang
Domain Adaptation (DA) aims to leverage the knowledge learned from a source domain with ample labeled data to a target domain with unlabeled data only. Most existing studies on DA…
Domain Adaptation by Maximizing Population Correlation with Neural Architecture Search
Zhixiong Yue, Pengxin Guo, Yu Zhang
In Domain Adaptation (DA), where the feature distributions of the source and target domains are different, various distance-based methods have been proposed to minimize the discrep…
Multi-Objective Meta Learning
Feiyang Ye, Baijiong Lin, Zhixiong Yue +3
Meta learning with multiple objectives can be formulated as a Multi-Objective Bi-Level optimization Problem (MOBLP) where the upper-level subproblem is to solve several possible co…
Multi-Task Adversarial Attack
Pengxin Guo, Yuancheng Xu, Baijiong Lin +1
Deep neural networks have achieved impressive performance in various areas, but they are shown to be vulnerable to adversarial attacks. Previous works on adversarial attacks mainly…
Deep Multi-Task Augmented Feature Learning via Hierarchical Graph Neural Network
Pengxin Guo, Chang Deng, Linjie Xu +2
Deep multi-task learning attracts much attention in recent years as it achieves good performance in many applications. Feature learning is important to deep multi-task learning for…