Zobrazeno 1 - 10
of 181
pro vyhledávání: '"Huang, Zijie"'
Recent advancements in Large Language Models (LLMs) have empowered LLM agents to autonomously collect world information, over which to conduct reasoning to solve complex problems. Given this capability, increasing interests have been put into employi
Externí odkaz:
http://arxiv.org/abs/2407.01231
Autor:
Han, Kaiqiao, Yang, Yi, Huang, Zijie, Kan, Xuan, Yang, Yang, Guo, Ying, He, Lifang, Zhan, Liang, Sun, Yizhou, Wang, Wei, Yang, Carl
Brain network analysis is vital for understanding the neural interactions regarding brain structures and functions, and identifying potential biomarkers for clinical phenotypes. However, widely used brain signals such as Blood Oxygen Level Dependent
Externí odkaz:
http://arxiv.org/abs/2405.00077
Autor:
Huang, Zijie, Hwang, Jeehyun, Zhang, Junkai, Baik, Jinwoo, Zhang, Weitong, Wodarz, Dominik, Sun, Yizhou, Gu, Quanquan, Wang, Wei
Real-world multi-agent systems are often dynamic and continuous, where the agents co-evolve and undergo changes in their trajectories and interactions over time. For example, the COVID-19 transmission in the U.S. can be viewed as a multi-agent system
Externí odkaz:
http://arxiv.org/abs/2403.00178
Autor:
Huang, Zijie, Li, Baolin, Asgharzadeh, Hafez, Cocos, Anne, Liu, Lingyi, Cox, Evan, Wise, Colby, Lamkhede, Sudarshan
Given a set of candidate entities (e.g. movie titles), the ability to identify similar entities is a core capability of many recommender systems. Most often this is achieved by collaborative filtering approaches, i.e. if users co-engage with a pair o
Externí odkaz:
http://arxiv.org/abs/2312.04071
Autor:
Huang, Zijie, Zhao, Wanjia, Gao, Jingdong, Hu, Ziniu, Luo, Xiao, Cao, Yadi, Chen, Yuanzhou, Sun, Yizhou, Wang, Wei
Learning complex multi-agent system dynamics from data is crucial across many domains, such as in physical simulations and material modeling. Extended from purely data-driven approaches, existing physics-informed approaches such as Hamiltonian Neural
Externí odkaz:
http://arxiv.org/abs/2310.06427
Generative large language models (LLMs) have shown great success in various applications, including question-answering (QA) and dialogue systems. However, in specialized domains like traditional Chinese medical QA, these models may perform unsatisfac
Externí odkaz:
http://arxiv.org/abs/2309.01114
Publikováno v:
KDD 2023
Learning multi-agent system dynamics has been extensively studied for various real-world applications, such as molecular dynamics in biology. Most of the existing models are built to learn single system dynamics from observed historical data and pred
Externí odkaz:
http://arxiv.org/abs/2307.04287
Autor:
Huang, Zijie, Wang, Daheng, Huang, Binxuan, Zhang, Chenwei, Shang, Jingbo, Liang, Yan, Wang, Zhengyang, Li, Xian, Faloutsos, Christos, Sun, Yizhou, Wang, Wei
Publikováno v:
ACL 2023
Knowledge graph embeddings (KGE) have been extensively studied to embed large-scale relational data for many real-world applications. Existing methods have long ignored the fact many KGs contain two fundamentally different views: high-level ontology-
Externí odkaz:
http://arxiv.org/abs/2307.01933
Autor:
Zhang, Shichang, Sohrabizadeh, Atefeh, Wan, Cheng, Huang, Zijie, Hu, Ziniu, Wang, Yewen, Yingyan, Lin, Cong, Jason, Sun, Yizhou
Graph neural networks (GNNs) are emerging for machine learning research on graph-structured data. GNNs achieve state-of-the-art performance on many tasks, but they face scalability challenges when it comes to real-world applications that have numerou
Externí odkaz:
http://arxiv.org/abs/2306.14052
Publikováno v:
KDD 2023
Multi-agent dynamical systems refer to scenarios where multiple units interact with each other and evolve collectively over time. To make informed decisions in multi-agent dynamical systems, such as determining the optimal vaccine distribution plan,
Externí odkaz:
http://arxiv.org/abs/2306.11216