103 citations · 484 across the 62 of their papers we have counts for
5 papers · 1 filter
Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning
Danqing Wang, Antonis Antoniades, Kha-Dinh Luong +6
Counterfactual explanations of Graph Neural Networks (GNNs) offer a powerful way to understand data that can naturally be represented by a graph structure. Furthermore, in many dom…
Generative Enzyme Design Guided by Functionally Important Sites and Small-Molecule Substrates
Zhenqiao Song, Yunlong Zhao, Wenxian Shi +3
Enzymes are genetically encoded biocatalysts capable of accelerating chemical reactions. How can we automatically design functional enzymes? In this paper, we propose EnzyGen, an a…
Follow Your Path: a Progressive Method for Knowledge Distillation
Wenxian Shi, Yuxuan Song, Hao Zhou +2
Deep neural networks often have a huge number of parameters, which posts challenges in deployment in application scenarios with limited memory and computation capacity. Knowledge d…
Adversarial Option-Aware Hierarchical Imitation Learning
Mingxuan Jing, Wenbing Huang, Fuchun Sun +4
It has been a challenge to learning skills for an agent from long-horizon unannotated demonstrations. Existing approaches like Hierarchical Imitation Learning(HIL) are prone to com…
BRITS: Bidirectional Recurrent Imputation for Time Series
Wei Cao, Dong Wang, Jian Li +3
Time series are widely used as signals in many classification/regression tasks. It is ubiquitous that time series contains many missing values. Given multiple correlated time serie…