2 code implementations • 25 Apr 2024 • Haizhou Shi, Zihao Xu, Hengyi Wang, Weiyi Qin, Wenyuan Wang, Yibin Wang, Hao Wang
In this survey, we provide a comprehensive overview of the current research progress on LLMs within the context of CL.
no code implementations • 21 Feb 2024 • Zihao Xu, Yi Liu, Gelei Deng, Yuekang Li, Stjepan Picek
Large Language Models (LLMS) have increasingly become central to generating content with potential societal impacts.
no code implementations • 12 Oct 2023 • Zihao Xu, Xuan Tang, Yufei Shi, Jianfeng Zhang, Jian Yang, Mingsong Chen, Xian Wei
To address this problem, we propose a novel replay strategy called Manifold Expansion Replay (MaER).
2 code implementations • 13 Jun 2023 • Tianyi Liu, Zihao Xu, Hao He, Guang-Yuan Hao, Guang-He Lee, Hao Wang
Domain adaptation aims to mitigate distribution shifts among different domains.
4 code implementations • 6 Feb 2023 • Zihao Xu, Guang-Yuan Hao, Hao He, Hao Wang
To address this challenge, we first provide a formal definition of domain index from the probabilistic perspective, and then propose an adversarial variational Bayesian framework that infers domain indices from multi-domain data, thereby providing additional insight on domain relations and improving domain adaptation performance.
1 code implementation • ICLR 2022 • Zihao Xu, Hao He, Guang-He Lee, Yuyang Wang, Hao Wang
In this work, we relax such uniform alignment by using a domain graph to encode domain adjacency, e. g., a graph of states in the US with each state as a domain and each edge indicating adjacency, thereby allowing domains to align flexibly based on the graph structure.
no code implementations • 29 Jan 2019 • Yutong Xie, Haiyang Wang, Yan Hao, Zihao Xu
In this paper, we propose a data-driven visual rhythm prediction method, which overcomes the previous works' deficiency that predictions are made primarily by human-crafted hard rules.