Zobrazeno 1 - 10
of 339
pro vyhledávání: '"Li, Lanqing"'
Offline meta reinforcement learning (OMRL) has emerged as a promising approach for interaction avoidance and strong generalization performance by leveraging pre-collected data and meta-learning techniques. Previous context-based approaches predominan
Externí odkaz:
http://arxiv.org/abs/2405.12001
Autor:
Chen, Kexin, Cao, Hanqun, Li, Junyou, Du, Yuyang, Guo, Menghao, Zeng, Xin, Li, Lanqing, Qiu, Jiezhong, Heng, Pheng Ann, Chen, Guangyong
Chemical synthesis, which is crucial for advancing material synthesis and drug discovery, impacts various sectors including environmental science and healthcare. The rise of technology in chemistry has generated extensive chemical data, challenging r
Externí odkaz:
http://arxiv.org/abs/2402.12993
As a marriage between offline RL and meta-RL, the advent of offline meta-reinforcement learning (OMRL) has shown great promise in enabling RL agents to multi-task and quickly adapt while acquiring knowledge safely. Among which, Context-based OMRL (CO
Externí odkaz:
http://arxiv.org/abs/2402.02429
Autor:
Chen, Kexin, Li, Junyou, Wang, Kunyi, Du, Yuyang, Yu, Jiahui, Lu, Jiamin, Li, Lanqing, Qiu, Jiezhong, Pan, Jianzhang, Huang, Yi, Fang, Qun, Heng, Pheng Ann, Chen, Guangyong
Recent AI research plots a promising future of automatic chemical reactions within the chemistry society. This study proposes Chemist-X, a transformative AI agent that automates the reaction condition recommendation (RCR) task in chemical synthesis w
Externí odkaz:
http://arxiv.org/abs/2311.10776
How to effectively represent molecules is a long-standing challenge for molecular property prediction and drug discovery. This paper studies this problem and proposes to incorporate chemical domain knowledge, specifically related to chemical reaction
Externí odkaz:
http://arxiv.org/abs/2305.01912
Autor:
Han, Zongbo, Liang, Zhipeng, Yang, Fan, Liu, Liu, Li, Lanqing, Bian, Yatao, Zhao, Peilin, Hu, Qinghua, Wu, Bingzhe, Zhang, Changqing, Yao, Jianhua
Subpopulation shift exists widely in many real-world applications, which refers to the training and test distributions that contain the same subpopulation groups but with different subpopulation proportions. Ignoring subpopulation shifts may lead to
Externí odkaz:
http://arxiv.org/abs/2304.04148
Reinforcement learning (RL) has shown promise for decision-making tasks in real-world applications. One practical framework involves training parameterized policy models from an offline dataset and subsequently deploying them in an online environment
Externí odkaz:
http://arxiv.org/abs/2303.07046
Publikováno v:
发电技术, Vol 45, Iss 4, Pp 684-695 (2024)
ObjectivesDistributed photovoltaic power prediction is of great significance for the operation and scheduling of photovoltaic power plants. Point prediction methods are difficult to comprehensively describe the uncertainty of distributed photovoltaic
Externí odkaz:
https://doaj.org/article/53432ce182f54b819d4d0d2cde8f49a7
Autor:
Gao, Ziqi, Niu, Yifan, Cheng, Jiashun, Tang, Jianheng, Xu, Tingyang, Zhao, Peilin, Li, Lanqing, Tsung, Fugee, Li, Jia
Graph neural networks (GNNs) are popular weapons for modeling relational data. Existing GNNs are not specified for attribute-incomplete graphs, making missing attribute imputation a burning issue. Until recently, many works notice that GNNs are coupl
Externí odkaz:
http://arxiv.org/abs/2211.16771
Autor:
Han, Zongbo, Liang, Zhipeng, Yang, Fan, Liu, Liu, Li, Lanqing, Bian, Yatao, Zhao, Peilin, Wu, Bingzhe, Zhang, Changqing, Yao, Jianhua
Subpopulation shift widely exists in many real-world machine learning applications, referring to the training and test distributions containing the same subpopulation groups but varying in subpopulation frequencies. Importance reweighting is a normal
Externí odkaz:
http://arxiv.org/abs/2209.08928