Zobrazeno 1 - 10
of 1 750
pro vyhledávání: '"YU, Jeffrey"'
Query-driven learned estimators are accurate, flexible, and lightweight alternatives to traditional estimators in query optimization. However, existing query-driven approaches struggle with the Out-of-distribution (OOD) problem, where the test worklo
Externí odkaz:
http://arxiv.org/abs/2412.05864
Autor:
Zhang, Guibin, Yue, Yanwei, Li, Zhixun, Yun, Sukwon, Wan, Guancheng, Wang, Kun, Cheng, Dawei, Yu, Jeffrey Xu, Chen, Tianlong
Recent advancements in large language model (LLM)-powered agents have shown that collective intelligence can significantly outperform individual capabilities, largely attributed to the meticulously designed inter-agent communication topologies. Thoug
Externí odkaz:
http://arxiv.org/abs/2410.02506
Autor:
Yang, Shuo, Xie, Jiadong, Liu, Yingfan, Yu, Jeffrey Xu, Gao, Xiyue, Wang, Qianru, Peng, Yanguo, Cui, Jiangtao
Proximity graphs (PG) have gained increasing popularity as the state-of-the-art (SOTA) solutions to $k$-approximate nearest neighbor ($k$-ANN) search on high-dimensional data, which serves as a fundamental function in various fields, e.g. information
Externí odkaz:
http://arxiv.org/abs/2410.01231
Autor:
Chen, Dingshuo, Li, Zhixun, Ni, Yuyan, Zhang, Guibin, Wang, Ding, Liu, Qiang, Wu, Shu, Yu, Jeffrey Xu, Wang, Liang
With the emergence of various molecular tasks and massive datasets, how to perform efficient training has become an urgent yet under-explored issue in the area. Data pruning (DP), as an oft-stated approach to saving training burdens, filters out less
Externí odkaz:
http://arxiv.org/abs/2409.01081
Autor:
Zhou, Yijie, Gong, Shufeng, Yao, Feng, Chen, Hanzhang, Yu, Song, Liu, Pengxi, Zhang, Yanfeng, Yu, Ge, Yu, Jeffrey Xu
Enhancing the efficiency of iterative computation on graphs has garnered considerable attention in both industry and academia. Nonetheless, the majority of efforts focus on expediting iterative computation by minimizing the running time per iteration
Externí odkaz:
http://arxiv.org/abs/2407.14544
Driven by the powerful representation ability of Graph Neural Networks (GNNs), plentiful GNN models have been widely deployed in many real-world applications. Nevertheless, due to distribution disparities between different demographic groups, fairnes
Externí odkaz:
http://arxiv.org/abs/2407.11624
Autor:
Cosentino, Justin, Belyaeva, Anastasiya, Liu, Xin, Furlotte, Nicholas A., Yang, Zhun, Lee, Chace, Schenck, Erik, Patel, Yojan, Cui, Jian, Schneider, Logan Douglas, Bryant, Robby, Gomes, Ryan G., Jiang, Allen, Lee, Roy, Liu, Yun, Perez, Javier, Rogers, Jameson K., Speed, Cathy, Tailor, Shyam, Walker, Megan, Yu, Jeffrey, Althoff, Tim, Heneghan, Conor, Hernandez, John, Malhotra, Mark, Stern, Leor, Matias, Yossi, Corrado, Greg S., Patel, Shwetak, Shetty, Shravya, Zhan, Jiening, Prabhakara, Shruthi, McDuff, Daniel, McLean, Cory Y.
In health, most large language model (LLM) research has focused on clinical tasks. However, mobile and wearable devices, which are rarely integrated into such tasks, provide rich, longitudinal data for personal health monitoring. Here we present Pers
Externí odkaz:
http://arxiv.org/abs/2406.06474
Publikováno v:
ICDE 2025
Cold-start rating prediction is a fundamental problem in recommender systems that has been extensively studied. Many methods have been proposed that exploit explicit relations among existing data, such as collaborative filtering, social recommendatio
Externí odkaz:
http://arxiv.org/abs/2403.17740
With the development of foundation models such as large language models, zero-shot transfer learning has become increasingly significant. This is highlighted by the generative capabilities of NLP models like GPT-4, and the retrieval-based approaches
Externí odkaz:
http://arxiv.org/abs/2402.11235
Graph plays a significant role in representing and analyzing complex relationships in real-world applications such as citation networks, social networks, and biological data. Recently, Large Language Models (LLMs), which have achieved tremendous succ
Externí odkaz:
http://arxiv.org/abs/2311.12399