no code implementations • 22 Dec 2023 • Bobin Yang, Jie Deng, Zhenghan Chen, Ruoxue Wu
The task of deducing three-dimensional molecular configurations from their two-dimensional graph representations holds paramount importance in the fields of computational chemistry and pharmaceutical development.
no code implementations • 21 Dec 2023 • Siyang Luo, Ziyi Jiang, Zhenghan Chen, Xiaoxuan Liang
Moreover, our approach incorporates adaptive perturbations into the dual branches, which align the source and target distribution to address domain discrepancies.
no code implementations • 15 Dec 2023 • Nan Yin, Mengzhu Wang, Zhenghan Chen, Giulia De Masi, Bin Gu, Huan Xiong
Current work often uses SNNs instead of Recurrent Neural Networks (RNNs) by using binary features instead of continuous ones for efficient training, which would overlooks graph structure information and leads to the loss of details during propagation.
no code implementations • 2 Dec 2023 • Xunzhu Tang, Zhenghan Chen, Kisub Kim, Haoye Tian, Saad Ezzini, Jacques Klein
To address this pressing issue, we introduce a novel security patch detection system, LLMDA, which capitalizes on Large Language Models (LLMs) and code-text alignment methodologies for patch review, data enhancement, and feature combination.