Zobrazeno 1 - 10
of 434
pro vyhledávání: '"Nevin, L"'
In multi-agent learning, the predominant approach focuses on generalization, often neglecting the optimization of individual agents. This emphasis on generalization limits the ability of agents to utilize their unique strengths, resulting in ineffici
Externí odkaz:
http://arxiv.org/abs/2410.02128
Autor:
Zhou, Xingzhi, Dong, Xin, Li, Chunhao, Bai, Yuning, Xu, Yulong, Cheung, Ka Chun, See, Simon, Song, Xinpeng, Zhang, Runshun, Zhou, Xuezhong, Zhang, Nevin L.
Traditional Chinese medicine (TCM) relies on specific combinations of herbs in prescriptions to treat symptoms and signs, a practice that spans thousands of years. Predicting TCM prescriptions presents a fascinating technical challenge with practical
Externí odkaz:
http://arxiv.org/abs/2407.10510
Test-time domain adaptation effectively adjusts the source domain model to accommodate unseen domain shifts in a target domain during inference. However, the model performance can be significantly impaired by continuous distribution changes in the ta
Externí odkaz:
http://arxiv.org/abs/2401.14619
Out-of-distribution (OOD) generalization is a complicated problem due to the idiosyncrasies of possible distribution shifts between training and test domains. Most benchmarks employ diverse datasets to address this issue; however, the degree of the d
Externí odkaz:
http://arxiv.org/abs/2310.06622
Autor:
Zhao, Yingxiu, Yu, Bowen, Hui, Binyuan, Yu, Haiyang, Huang, Fei, Li, Yongbin, Zhang, Nevin L.
Training large language models (LLMs) with open-domain instruction data has yielded remarkable success in aligning to end tasks and human preferences. Extensive research has highlighted the importance of the quality and diversity of instruction data.
Externí odkaz:
http://arxiv.org/abs/2308.05696
Autor:
Zhang, Nevin L., Li, Kaican, Gao, Han, Xie, Weiyan, Lin, Zhi, Li, Zhenguo, Wang, Luning, Huang, Yongxiang
Domain generalization (DG) is about learning models that generalize well to new domains that are related to, but different from, the training domain(s). It is a fundamental problem in machine learning and has attracted much attention in recent years.
Externí odkaz:
http://arxiv.org/abs/2307.06825
The need to explain the output of a deep neural network classifier is now widely recognized. While previous methods typically explain a single class in the output, we advocate explaining the whole output, which is a probability distribution over mult
Externí odkaz:
http://arxiv.org/abs/2306.06339
Autor:
Zhao, Yingxiu, Yu, Bowen, Yu, Haiyang, Li, Bowen, Li, Jinyang, Wang, Chao, Huang, Fei, Li, Yongbin, Zhang, Nevin L.
The goal of document-grounded dialogue (DocGD) is to generate a response by grounding the evidence in a supporting document in accordance with the dialogue context. This process involves four variables that are causally connected. Recently, task-spec
Externí odkaz:
http://arxiv.org/abs/2305.10927
Autor:
Gao, Han, Li, Kaican, Xie, Weiyan, Lin, Zhi, Huang, Yongxiang, Wang, Luning, Cao, Caleb Chen, Zhang, Nevin L.
Domain generalization (DG) is about training models that generalize well under domain shift. Previous research on DG has been conducted mostly in single-source or multi-source settings. In this paper, we consider a third, lesser-known setting where a
Externí odkaz:
http://arxiv.org/abs/2305.07888
Autor:
Zhao, Yingxiu, Zheng, Yinhe, Yu, Bowen, Tian, Zhiliang, Lee, Dongkyu, Sun, Jian, Yu, Haiyang, Li, Yongbin, Zhang, Nevin L.
Lifelong learning aims to accumulate knowledge and alleviate catastrophic forgetting when learning tasks sequentially. However, existing lifelong language learning methods only focus on the supervised learning setting. Unlabeled data, which can be ea
Externí odkaz:
http://arxiv.org/abs/2211.13050