no code implementations • 6 Mar 2024 • Kaiwei Zhang, Yange Lin, Guangcheng Wu, Yuxiang Ren, Xuecang Zhang, Bo wang, XiaoYu Zhang, Weitao Du
This work not only holds general significance for the advancement of deep learning methodologies but also paves the way for a transformative shift in molecular design strategies.
no code implementations • 3 Jan 2024 • Weitao Du, Shengchao Liu, Xuecang Zhang
By conceiving physical systems as 3D many-body point clouds, geometric graph neural networks (GNNs), such as SE(3)/E(3) equivalent GNNs, have showcased promising performance.
no code implementations • NeurIPS 2023 • Weitao Du, Jiujiu Chen, Xuecang Zhang, ZhiMing Ma, Shengchao Liu
The fundamental building block for drug discovery is molecule geometry and thus, the molecule's geometrical representation is the main bottleneck to better utilize machine learning techniques for drug discovery.
no code implementations • 5 Dec 2023 • Chaoyi Chen, Dechao Gao, Yanfeng Zhang, Qiange Wang, Zhenbo Fu, Xuecang Zhang, Junhua Zhu, Yu Gu, Ge Yu
Though many dynamic GNN models have emerged to learn from evolving graphs, the training process of these dynamic GNNs is dramatically different from traditional GNNs in that it captures both the spatial and temporal dependencies of graph updates.
no code implementations • 24 Nov 2023 • Shengyin Sun, Yuxiang Ren, Chen Ma, Xuecang Zhang
The latest advancements in large language models (LLMs) have revolutionized the field of natural language processing (NLP).
no code implementations • 15 Oct 2023 • Jianxiang Yu, Yuxiang Ren, Chenghua Gong, Jiaqi Tan, Xiang Li, Xuecang Zhang
In order to tackle this challenge, we propose a lightweight paradigm called ENG, which adopts a plug-and-play approach to empower text-attributed graphs through node generation using LLMs.
no code implementations • 30 Sep 2023 • Jiongkang Ni, Xiaoliang Xu, Yuxiang Wang, Can Li, Jiajie Yao, Shihai Xiao, Xuecang Zhang
The main drawback of graph-based ANNS is that a graph index would be too large to fit into the memory especially for a large-scale $\mathcal{X}$.
1 code implementation • 5 Nov 2021 • Tianyu Zhang, Yuxiang Ren, Wenzheng Feng, Weitao Du, Xuecang Zhang
In this paper, we show the potential hazards of inappropriate augmentations and then propose a novel Collaborative Graph Contrastive Learning framework (CGCL).