Zobrazeno 1 - 10
of 6 697
pro vyhledávání: '"Wang, WenJun"'
Domain generalization on graphs aims to develop models with robust generalization capabilities, ensuring effective performance on the testing set despite disparities between testing and training distributions. However, existing methods often rely on
Externí odkaz:
http://arxiv.org/abs/2411.12913
Graph similarity computation (GSC) aims to quantify the similarity score between two graphs. Although recent GSC methods based on graph neural networks (GNNs) take advantage of intra-graph structures in message passing, few of them fully utilize the
Externí odkaz:
http://arxiv.org/abs/2411.03624
Autor:
Zhang, Yiming, He, Baoyi, Zhang, Shengyu, Fu, Yuhao, Zhou, Qi, Sang, Zhijie, Hong, Zijin, Yang, Kejing, Wang, Wenjun, Yuan, Jianbo, Ning, Guanghan, Li, Linyi, Ji, Chunlin, Wu, Fei, Yang, Hongxia
Recent advancements in building domain-specific large language models (LLMs) have shown remarkable success, especially in tasks requiring reasoning abilities like logical inference over complex relationships and multi-step problem solving. However, c
Externí odkaz:
http://arxiv.org/abs/2410.13699
The rapid advancement of Multimodal Large Language Models (MLLMs) has led to remarkable performances across various domains. However, this progress is accompanied by a substantial surge in the resource consumption of these models. We address this pre
Externí odkaz:
http://arxiv.org/abs/2409.10994
Achieving the generalization of an invariant classifier from training domains to shifted test domains while simultaneously considering model fairness is a substantial and complex challenge in machine learning. Existing methods address the problem of
Externí odkaz:
http://arxiv.org/abs/2408.09312
Product review generation is an important task in recommender systems, which could provide explanation and persuasiveness for the recommendation. Recently, Large Language Models (LLMs, e.g., ChatGPT) have shown superior text modeling and generating a
Externí odkaz:
http://arxiv.org/abs/2407.07487
Autor:
Liu, Lin, Zhao, Jian, Hu, Cheng, Cao, Zhengtao, Zhao, Youpeng, Ye, Zhenbin, Meng, Meng, Wang, Wenjun, He, Zhaofeng, Li, Houqiang, Lin, Xia, Huang, Lanxiao
Games are widely used as research environments for multi-agent reinforcement learning (MARL), but they pose three significant challenges: limited customization, high computational demands, and oversimplification. To address these issues, we introduce
Externí odkaz:
http://arxiv.org/abs/2406.03978
Publikováno v:
JMIR mHealth and uHealth, Vol 8, Iss 10, p e19589 (2020)
BackgroundA novel coronavirus, SARS-CoV-2, was identified in December 2019, when the first cases were reported in Wuhan, China. The once-localized outbreak has since been declared a pandemic. As of April 24, 2020, there have been 2.7 million confirme
Externí odkaz:
https://doaj.org/article/72bfef26c9d648f886763f487e1c057c
Traditional machine learning methods heavily rely on the independent and identically distribution assumption, which imposes limitations when the test distribution deviates from the training distribution. To address this crucial issue, out-of-distribu
Externí odkaz:
http://arxiv.org/abs/2403.16334
Finding experts is essential in Community Question Answering (CQA) platforms as it enables the effective routing of questions to potential users who can provide relevant answers. The key is to personalized learning expert representations based on the
Externí odkaz:
http://arxiv.org/abs/2312.12162