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of 5
pro vyhledávání: '"Ding, Qianggang"'
Material discovery is a critical research area with profound implications for various industries. In this work, we introduce MatExpert, a novel framework that leverages Large Language Models (LLMs) and contrastive learning to accelerate the discovery
Externí odkaz:
http://arxiv.org/abs/2410.21317
The integration of Artificial Intelligence (AI) in the financial domain has opened new avenues for quantitative trading, particularly through the use of Large Language Models (LLMs). However, the challenge of effectively synthesizing insights from di
Externí odkaz:
http://arxiv.org/abs/2411.00782
Graph neural networks (GNNs) have been applied into a variety of graph tasks. Most existing work of GNNs is based on the assumption that the given graph data is optimal, while it is inevitable that there exists missing or incomplete edges in the grap
Externí odkaz:
http://arxiv.org/abs/2205.05964
Autor:
Yan, Chaochao, Ding, Qianggang, Zhao, Peilin, Zheng, Shuangjia, Yang, Jinyu, Yu, Yang, Huang, Junzhou
Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate or assist in the retrosynthesis analysis, various retr
Externí odkaz:
http://arxiv.org/abs/2011.02893
Recently, a variety of regularization techniques have been widely applied in deep neural networks, such as dropout, batch normalization, data augmentation, and so on. These methods mainly focus on the regularization of weight parameters to prevent ov
Externí odkaz:
http://arxiv.org/abs/1908.05474