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2020 – today
- 2024
[j3]Feiyang Ye
, Baijiong Lin, Zhixiong Yue, Yu Zhang, Ivor W. Tsang:
Multi-objective meta-learning. Artif. Intell. 335: 104184 (2024)- [c5]Feiyang Ye, Baijiong Lin, Xiaofeng Cao, Yu Zhang, Ivor W. Tsang:
A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization. ECAI 2024: 2621-2628 - [c4]Baijiong Lin, Weisen Jiang, Pengguang Chen, Yu Zhang, Shu Liu, Ying-Cong Chen:
MTMamba: Enhancing Multi-task Dense Scene Understanding by Mamba-Based Decoders. ECCV (70) 2024: 314-330 - [c3]Xiyu Wang, Baijiong Lin, Daochang Liu, Ying-Cong Chen, Chang Xu:
Bridging Data Gaps in Diffusion Models with Adversarial Noise-Based Transfer Learning. ICML 2024 - [i12]Feiyang Ye, Baijiong Lin, Xiaofeng Cao, Yu Zhang, Ivor W. Tsang:
A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization. CoRR abs/2401.09257 (2024) - [i11]Baijiong Lin, Weisen Jiang, Pengguang Chen, Yu Zhang, Shu Liu, Ying-Cong Chen:
MTMamba: Enhancing Multi-Task Dense Scene Understanding by Mamba-Based Decoders. CoRR abs/2407.02228 (2024) - [i10]Baijiong Lin, Weisen Jiang, Pengguang Chen, Shu Liu, Ying-Cong Chen:
MTMamba++: Enhancing Multi-Task Dense Scene Understanding via Mamba-Based Decoders. CoRR abs/2408.15101 (2024) - [i9]Shuhao Chen, Weisen Jiang, Baijiong Lin, James T. Kwok, Yu Zhang:
RouterDC: Query-Based Router by Dual Contrastive Learning for Assembling Large Language Models. CoRR abs/2409.19886 (2024) - 2023
- [j2]Baijiong Lin, Yu Zhang:
LibMTL: A Python Library for Deep Multi-Task Learning. J. Mach. Learn. Res. 24: 209:1-209:7 (2023) - [i8]Xiyu Wang, Baijiong Lin, Daochang Liu, Chang Xu:
Efficient Transfer Learning in Diffusion Models via Adversarial Noise. CoRR abs/2308.11948 (2023) - [i7]Baijiong Lin, Weisen Jiang, Feiyang Ye, Yu Zhang, Pengguang Chen, Ying-Cong Chen, Shu Liu:
A Scale-Invariant Task Balancing Approach for Multi-Task Learning. CoRR abs/2308.12029 (2023) - [i6]Weisen Jiang, Baijiong Lin, Han Shi, Yu Zhang, Zhenguo Li, James T. Kwok:
Effective and Parameter-Efficient Reusing Fine-Tuned Models. CoRR abs/2310.01886 (2023) - 2022
- [j1]Baijiong Lin, Feiyang Ye, Yu Zhang, Ivor W. Tsang:
Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning. Trans. Mach. Learn. Res. 2022 (2022) - [c2]Zhixiong Yue, Baijiong Lin, Yu Zhang, Christy Jie Liang:
Effective, Efficient and Robust Neural Architecture Search. IJCNN 2022: 1-8 - [i5]Baijiong Lin, Yu Zhang:
LibMTL: A Python Library for Multi-Task Learning. CoRR abs/2203.14338 (2022) - 2021
- [c1]Feiyang Ye, Baijiong Lin, Zhixiong Yue, Pengxin Guo, Qiao Xiao, Yu Zhang:
Multi-Objective Meta Learning. NeurIPS 2021: 21338-21351 - [i4]Feiyang Ye, Baijiong Lin, Zhixiong Yue, Pengxin Guo, Qiao Xiao, Yu Zhang:
Multi-Objective Meta Learning. CoRR abs/2102.07121 (2021) - [i3]Baijiong Lin, Feiyang Ye, Yu Zhang:
A Closer Look at Loss Weighting in Multi-Task Learning. CoRR abs/2111.10603 (2021) - 2020
- [i2]Zhixiong Yue, Baijiong Lin, Xiaonan Huang, Yu Zhang:
Effective, Efficient and Robust Neural Architecture Search. CoRR abs/2011.09820 (2020) - [i1]Pengxin Guo, Yuancheng Xu, Baijiong Lin, Yu Zhang:
Multi-Task Adversarial Attack. CoRR abs/2011.09824 (2020)
Coauthor Index
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