6 citations · 11 across the 3 of their papers we have counts for
7 papers
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs
Zhao Xu, Youzhi Luo, Xuan Zhang +7
Graph neural networks are emerging as promising methods for modeling molecular graphs, in which nodes and edges correspond to atoms and chemical bonds, respectively. Recent studies…
Group Contrastive Self-Supervised Learning on Graphs
Xinyi Xu, Cheng Deng, Yaochen Xie +1
We study self-supervised learning on graphs using contrastive methods. A general scheme of prior methods is to optimize two-view representations of input graphs. In many studies, a…
Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
Meng Liu, Cong Fu, Xuan Zhang +7
Molecular property prediction is gaining increasing attention due to its diverse applications. One task of particular interests and importance is to predict quantum chemical proper…
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Meng Liu, Youzhi Luo, Limei Wang +13
Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning. In the research community, implementin…
Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery
Zhengyang Wang, Meng Liu, Youzhi Luo +8
Properties of molecules are indicative of their functions and thus are useful in many applications. With the advances of deep learning methods, computational approaches for predict…
Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising
Yaochen Xie, Zhengyang Wang, Shuiwang Ji
Self-supervised frameworks that learn denoising models with merely individual noisy images have shown strong capability and promising performance in various image denoising tasks.…