5 citations · 9 across the 4 of their papers we have counts for
4 papers
CandidateDrug4Cancer: An Open Molecular Graph Learning Benchmark on Drug Discovery for Cancer
Xianbin Ye, Ziliang Li, Fei Ma +6
Anti-cancer drug discoveries have been serendipitous, we sought to present the Open Molecular Graph Learning Benchmark, named CandidateDrug4Cancer, a challenging and realistic benc…
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks
Pengyong Li, Jun Wang, Ziliang Li +6
Self-supervised learning has gradually emerged as a powerful technique for graph representation learning. However, transferable, generalizable, and robust representation learning o…
Winner Team Mia at TextVQA Challenge 2021: Vision-and-Language Representation Learning with Pre-trained Sequence-to-Sequence Model
Yixuan Qiao, Hao Chen, Jun Wang +7
TextVQA requires models to read and reason about text in images to answer questions about them. Specifically, models need to incorporate a new modality of text present in the image…
Interpretable discovery of new semiconductors with machine learning
Hitarth Choubisa, Petar Todorović, Joao M. Pina +5
Machine learning models of materials accelerate discovery compared to ab initio methods: deep learning models now reproduce density functional theory (DFT)-calculated resul…