39 citations · 92 across the 12 of their papers we have counts for
12 papers
Personalized Federated Learning with Local Attention
Sicong Liang, Junchao Tian, Shujun Yang +1
Federated Learning (FL) aims to learn a single global model that enables the central server to help the model training in local clients without accessing their local data. The key…
LibMTL: A Python Library for Multi-Task Learning
Baijiong Lin, Yu Zhang
This paper presents LibMTL, an open-source Python library built on PyTorch, which provides a unified, comprehensive, reproducible, and extensible implementation framework for Multi…
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…
Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning
Baijiong Lin, Feiyang Ye, Yu Zhang +1
Multi-Task Learning (MTL) has achieved success in various fields. However, how to balance different tasks to achieve good performance is a key problem. To achieve the task balancin…
Deep Safe Multi-Task Learning
Zhixiong Yue, Feiyang Ye, Yu Zhang +2
In recent years, Multi-Task Learning (MTL) has attracted much attention due to its good performance in many applications. However, many existing MTL models cannot guarantee that th…
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…
Effective, Efficient and Robust Neural Architecture Search
Zhixiong Yue, Baijiong Lin, Xiaonan Huang +1
Recent advances in adversarial attacks show the vulnerability of deep neural networks searched by Neural Architecture Search (NAS). Although NAS methods can find network architectu…
Distant Transfer Learning via Deep Random Walk
Qiao Xiao, Yu Zhang
Transfer learning, which is to improve the learning performance in the target domain by leveraging useful knowledge from the source domain, often requires that those two domains ar…
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…
A Simple General Approach to Balance Task Difficulty in Multi-Task Learning
Sicong Liang, Yu Zhang
In multi-task learning, difficulty levels of different tasks are varying. There are many works to handle this situation and we classify them into five categories, including the dir…