115 citations · 307 across the 9 of their papers we have counts for
10 papers
Qwen Technical Report
Jinze Bai, Shuai Bai, Yunfei Chu +45
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to hu…
Multi-task Bioassay Pre-training for Protein-ligand Binding Affinity Prediction
Jiaxian Yan, Zhaofeng Ye, Ziyi Yang +4
Protein-ligand binding affinity (PLBA) prediction is the fundamental task in drug discovery. Recently, various deep learning-based models predict binding affinity by incorporating…
Knowledge Distillation of Transformer-based Language Models Revisited
Chengqiang Lu, Jianwei Zhang, Yunfei Chu +5
In the past few years, transformer-based pre-trained language models have achieved astounding success in both industry and academia. However, the large model size and high run-time…
Deep Unified Representation for Heterogeneous Recommendation
Chengqiang Lu, Mingyang Yin, Shuheng Shen +3
Recommendation system has been a widely studied task both in academia and industry. Previous works mainly focus on homogeneous recommendation and little progress has been made for…
ProtGNN: Towards Self-Explaining Graph Neural Networks
Zaixi Zhang, Qi Liu, Hao Wang +2
Despite the recent progress in Graph Neural Networks (GNNs), it remains challenging to explain the predictions made by GNNs. Existing explanation methods mainly focus on post-hoc e…
Motif-based Graph Self-Supervised Learning for Molecular Property Prediction
Zaixi Zhang, Qi Liu, Hao Wang +2
Predicting molecular properties with data-driven methods has drawn much attention in recent years. Particularly, Graph Neural Networks (GNNs) have demonstrated remarkable success i…
GraphMI: Extracting Private Graph Data from Graph Neural Networks
Zaixi Zhang, Qi Liu, Zhenya Huang +4
As machine learning becomes more widely used for critical applications, the need to study its implications in privacy turns to be urgent. Given access to the target model and auxil…
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction
Zhongkai Hao, Chengqiang Lu, Zheyuan Hu +5
Molecular property prediction (e.g., energy) is an essential problem in chemistry and biology. Unfortunately, many supervised learning methods usually suffer from the problem of sc…
Deep Learning for Optoelectronic Properties of Organic Semiconductors
Chengqiang Lu, Qi Liu, Qiming Sun +4
Atomistic modeling of energetic disorder in organic semiconductors (OSCs) and its effects on the optoelectronic properties of OSCs requires a large number of excited-state electron…
Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective
Chengqiang Lu, Qi Liu, Chao Wang +3
Predicting molecular properties (e.g., atomization energy) is an essential issue in quantum chemistry, which could speed up much research progress, such as drug designing and subst…