24 citations · 41 across the 10 of their papers we have counts for
11 papers
Filter Pruning via Filters Similarity in Consecutive Layers
Xiaorui Wang, Jun Wang, Xin Tang +3
Filter pruning is widely adopted to compress and accelerate the Convolutional Neural Networks (CNNs), but most previous works ignore the relationship between filters and channels i…
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…
PASH at TREC 2021 Deep Learning Track: Generative Enhanced Model for Multi-stage Ranking
Yixuan Qiao, Shanshan Zhao, Jun Wang +8
This paper describes the PASH participation in TREC 2021 Deep Learning Track. In the recall stage, we adopt a scheme combining sparse and dense retrieval method. In the multi-stage…
6G Network AI Architecture for Everyone-Centric Customized Services
Yang Yang, Mulei Ma, Hequan Wu +51
Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to…
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…
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…