Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Zhang Ceyao"'
Autor:
Li Renjie, Zhang Ceyao, Xie Wentao, Gong Yuanhao, Ding Feilong, Dai Hui, Chen Zihan, Yin Feng, Zhang Zhaoyu
Publikováno v:
Nanophotonics, Vol 12, Iss 2, Pp 319-334 (2023)
Photonics inverse design relies on human experts to search for a design topology that satisfies certain optical specifications with their experience and intuitions, which is relatively labor-intensive, slow, and sub-optimal. Machine learning has emer
Externí odkaz:
https://doaj.org/article/85a90c81f6474747af2673bf2a1d29fe
Autor:
Chi, Yizhou, Lin, Yizhang, Hong, Sirui, Pan, Duyi, Fei, Yaying, Mei, Guanghao, Liu, Bangbang, Pang, Tianqi, Kwok, Jacky, Zhang, Ceyao, Liu, Bang, Wu, Chenglin
Automated Machine Learning (AutoML) approaches encompass traditional methods that optimize fixed pipelines for model selection and ensembling, as well as newer LLM-based frameworks that autonomously build pipelines. While LLM-based agents have shown
Externí odkaz:
http://arxiv.org/abs/2410.17238
Autor:
Wang, Zihao, Cai, Shaofei, Mu, Zhancun, Lin, Haowei, Zhang, Ceyao, Liu, Xuejie, Li, Qing, Liu, Anji, Ma, Xiaojian, Liang, Yitao
This paper presents OmniJARVIS, a novel Vision-Language-Action (VLA) model for open-world instruction-following agents in Minecraft. Compared to prior works that either emit textual goals to separate controllers or produce the control command directl
Externí odkaz:
http://arxiv.org/abs/2407.00114
Photonic Crystal Surface Emitting Lasers (PCSEL)'s inverse design demands expert knowledge in physics, materials science, and quantum mechanics which is prohibitively labor-intensive. Advanced AI technologies, especially reinforcement learning (RL),
Externí odkaz:
http://arxiv.org/abs/2403.05149
Autor:
Tan, Weihao, Zhang, Wentao, Xu, Xinrun, Xia, Haochong, Ding, Ziluo, Li, Boyu, Zhou, Bohan, Yue, Junpeng, Jiang, Jiechuan, Li, Yewen, An, Ruyi, Qin, Molei, Zong, Chuqiao, Zheng, Longtao, Wu, Yujie, Chai, Xiaoqiang, Bi, Yifei, Xie, Tianbao, Gu, Pengjie, Li, Xiyun, Zhang, Ceyao, Tian, Long, Wang, Chaojie, Wang, Xinrun, Karlsson, Börje F., An, Bo, Yan, Shuicheng, Lu, Zongqing
Despite the success in specific scenarios, existing foundation agents still struggle to generalize across various virtual scenarios, mainly due to the dramatically different encapsulations of environments with manually designed observation and action
Externí odkaz:
http://arxiv.org/abs/2403.03186
Autor:
Hong, Sirui, Lin, Yizhang, Liu, Bang, Liu, Bangbang, Wu, Binhao, Zhang, Ceyao, Wei, Chenxing, Li, Danyang, Chen, Jiaqi, Zhang, Jiayi, Wang, Jinlin, Zhang, Li, Zhang, Lingyao, Yang, Min, Zhuge, Mingchen, Guo, Taicheng, Zhou, Tuo, Tao, Wei, Tang, Xiangru, Lu, Xiangtao, Zheng, Xiawu, Liang, Xinbing, Fei, Yaying, Cheng, Yuheng, Gou, Zhibin, Xu, Zongze, Wu, Chenglin
Large Language Model (LLM)-based agents have shown effectiveness across many applications. However, their use in data science scenarios requiring solving long-term interconnected tasks, dynamic data adjustments and domain expertise remains challengin
Externí odkaz:
http://arxiv.org/abs/2402.18679
Autor:
Cheng, Yuheng, Zhang, Ceyao, Zhang, Zhengwen, Meng, Xiangrui, Hong, Sirui, Li, Wenhao, Wang, Zihao, Wang, Zekai, Yin, Feng, Zhao, Junhua, He, Xiuqiang
Intelligent agents stand out as a potential path toward artificial general intelligence (AGI). Thus, researchers have dedicated significant effort to diverse implementations for them. Benefiting from recent progress in large language models (LLMs), L
Externí odkaz:
http://arxiv.org/abs/2401.03428
Autor:
Zhang, Ceyao, Yang, Kaijie, Hu, Siyi, Wang, Zihao, Li, Guanghe, Sun, Yihang, Zhang, Cheng, Zhang, Zhaowei, Liu, Anji, Zhu, Song-Chun, Chang, Xiaojun, Zhang, Junge, Yin, Feng, Liang, Yitao, Yang, Yaodong
Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in the realm of multi-agent systems. Current approaches to developing cooperative agents rely primarily on learning-based methods, whose policy generalization depe
Externí odkaz:
http://arxiv.org/abs/2308.11339
Autor:
Hong, Sirui, Zhuge, Mingchen, Chen, Jiaqi, Zheng, Xiawu, Cheng, Yuheng, Zhang, Ceyao, Wang, Jinlin, Wang, Zili, Yau, Steven Ka Shing, Lin, Zijuan, Zhou, Liyang, Ran, Chenyu, Xiao, Lingfeng, Wu, Chenglin, Schmidhuber, Jürgen
Remarkable progress has been made on automated problem solving through societies of agents based on large language models (LLMs). Existing LLM-based multi-agent systems can already solve simple dialogue tasks. Solutions to more complex tasks, however
Externí odkaz:
http://arxiv.org/abs/2308.00352
Autor:
Zhang, Zhaowei, Zhang, Ceyao, Liu, Nian, Qi, Siyuan, Rong, Ziqi, Zhu, Song-Chun, Cui, Shuguang, Yang, Yaodong
The emergent capabilities of Large Language Models (LLMs) have made it crucial to align their values with those of humans. However, current methodologies typically attempt to assign value as an attribute to LLMs, yet lack attention to the ability to
Externí odkaz:
http://arxiv.org/abs/2305.17147