Zobrazeno 1 - 10
of 233
pro vyhledávání: '"Wei, Zhewei"'
Autor:
Ji, Jiarui, Li, Yang, Liu, Hongtao, Du, Zhicheng, Wei, Zhewei, Shen, Weiran, Qi, Qi, Lin, Yankai
Public scarce resource allocation plays a crucial role in economics as it directly influences the efficiency and equity in society. Traditional studies including theoretical model-based, empirical study-based and simulation-based methods encounter li
Externí odkaz:
http://arxiv.org/abs/2410.14152
Online Kernel Learning (OKL) has attracted considerable research interest due to its promising predictive performance in streaming environments. Second-order approaches are particularly appealing for OKL as they often offer substantial improvements i
Externí odkaz:
http://arxiv.org/abs/2410.11188
The utilization of sketching techniques has progressively emerged as a pivotal method for enhancing the efficiency of online learning. In linear bandit settings, current sketch-based approaches leverage matrix sketching to reduce the per-round time c
Externí odkaz:
http://arxiv.org/abs/2410.10258
Autor:
Ji, Jiarui, Lei, Runlin, Bi, Jialing, Wei, Zhewei, Lin, Yankai, Pan, Xuchen, Li, Yaliang, Ding, Bolin
Graph generation is a fundamental task that has been extensively studied in social, technological, and scientific analysis. For modeling the dynamic graph evolution process, traditional rule-based methods struggle to capture community structures with
Externí odkaz:
http://arxiv.org/abs/2410.09824
Recent research has explored the use of Large Language Models (LLMs) for tackling complex graph reasoning tasks. However, due to the intricacies of graph structures and the inherent limitations of LLMs in handling long text, current approaches often
Externí odkaz:
http://arxiv.org/abs/2410.05130
Graph Neural Networks (GNNs) are extensively employed in graph machine learning, with considerable research focusing on their expressiveness. Current studies often assess GNN expressiveness by comparing them to the Weisfeiler-Lehman (WL) tests or cla
Externí odkaz:
http://arxiv.org/abs/2410.01308
Virtual Screening is an essential technique in the early phases of drug discovery, aimed at identifying promising drug candidates from vast molecular libraries. Recently, ligand-based virtual screening has garnered significant attention due to its ef
Externí odkaz:
http://arxiv.org/abs/2409.07462
Autor:
Yi, Lu, Wei, Zhewei
Graph unlearning has emerged as a pivotal research area for ensuring privacy protection, given the widespread adoption of Graph Neural Networks (GNNs) in applications involving sensitive user data. Among existing studies, certified graph unlearning i
Externí odkaz:
http://arxiv.org/abs/2408.09212
The drastic performance degradation of Graph Neural Networks (GNNs) as the depth of the graph propagation layers exceeds 8-10 is widely attributed to a phenomenon of Over-smoothing. Although recent research suggests that Over-smoothing may not be the
Externí odkaz:
http://arxiv.org/abs/2408.03669
Autor:
Chen, Jie, Chen, Zhipeng, Wang, Jiapeng, Zhou, Kun, Zhu, Yutao, Jiang, Jinhao, Min, Yingqian, Zhao, Wayne Xin, Dou, Zhicheng, Mao, Jiaxin, Lin, Yankai, Song, Ruihua, Xu, Jun, Chen, Xu, Yan, Rui, Wei, Zhewei, Hu, Di, Huang, Wenbing, Wen, Ji-Rong
Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. To make the CPT approach more traceable, this paper presents a technical report for continually pre-training Llama-3 (8B), which si
Externí odkaz:
http://arxiv.org/abs/2407.18743