24 citations · 36 across the 5 of their papers we have counts for
5 papers
HCL: Improving Graph Representation with Hierarchical Contrastive Learning
Jun Wang, Weixun Li, Changyu Hou +6
Contrastive learning has emerged as a powerful tool for graph representation learning. However, most contrastive learning methods learn features of graphs with fixed coarse-grained…
SFE-AI at SemEval-2022 Task 11: Low-Resource Named Entity Recognition using Large Pre-trained Language Models
Changyu Hou, Jun Wang, Yixuan Qiao +8
Large scale pre-training models have been widely used in named entity recognition (NER) tasks. However, model ensemble through parameter averaging or voting can not give full play…
Learn molecular representations from large-scale unlabeled molecules for drug discovery
Pengyong Li, Jun Wang, Yixuan Qiao +6
How to produce expressive molecular representations is a fundamental challenge in AI-driven drug discovery. Graph neural network (GNN) has emerged as a powerful technique for model…
Semi-supervised Active Learning for Instance Segmentation via Scoring Predictions
Jun Wang, Shaoguo Wen, Kaixing Chen +5
Active learning generally involves querying the most representative samples for human labeling, which has been widely studied in many fields such as image classification and object…
Reconstruction Regularized Deep Metric Learning for Multi-label Image Classification
Changsheng Li, Chong Liu, Lixin Duan +2
In this paper, we present a novel deep metric learning method to tackle the multi-label image classification problem. In order to better learn the correlations among images feature…