306 citations · 829 across the 29 of their papers we have counts for
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cs.LG2023★ 4 cited
FABind: Fast and Accurate Protein-Ligand Binding
Qizhi Pei, Kaiyuan Gao, Lijun Wu +7
Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements i…
cs.LG2021★ 306 cited
R-Drop: Regularized Dropout for Neural Networks
Xiaobo Liang, Lijun Wu, Juntao Li +6
Dropout is a powerful and widely used technique to regularize the training of deep neural networks. In this paper, we introduce a simple regularization strategy upon dropout in mod…
cs.LG2020★ 3 cited
Masked Contrastive Representation Learning for Reinforcement Learning
Jinhua Zhu, Yingce Xia, Lijun Wu +4
Improving sample efficiency is a key research problem in reinforcement learning (RL), and CURL, which uses contrastive learning to extract high-level features from raw pixels of in…