activity
20202022
most citedSentiment Analysis and Topic Modeling for COVID-19 Vaccine Discussions

9 citations · 21 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2022★ 4 cited

Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing

Xiangyu Song, Jianxin Li, Qi Lei +3

The goal of Knowledge Tracing (KT) is to estimate how well students have mastered a concept based on their historical learning of related exercises. The benefit of knowledge tracin…

cs.SI2021

Analysis of 5G academic Network based on graph representation learning method

Xiaoming Li, Guangquan Xu, Wei Yu +2

With the rapid development of 5th Generation Mobile Communication Technology (5G), the diverse forms of collaboration and extensive data in academic social networks constructed by…

cs.CY2021★ 9 cited

Sentiment Analysis and Topic Modeling for COVID-19 Vaccine Discussions

Hui Yin, Xiangyu Song, Shuiqiao Yang +1

The outbreak of the novel Coronavirus Disease 2019 (COVID-19) has lasted for nearly two years and caused unprecedented impacts on people's daily life around the world. Even worse,…

cs.LG2021★ 2 cited

Distributed Optimization of Graph Convolutional Network using Subgraph Variance

Taige Zhao, Xiangyu Song, Jianxin Li +2

In recent years, Graph Convolutional Networks (GCNs) have achieved great success in learning from graph-structured data. With the growing tendency of graph nodes and edges, GCN tra…

cs.CL2021

Representation Learning for Short Text Clustering

Hui Yin, Xiangyu Song, Shuiqiao Yang +2

Effective representation learning is critical for short text clustering due to the sparse, high-dimensional and noise attributes of short text corpus. Existing pre-trained models (…

cs.CV2020★ 6 cited

Simultaneous Detection and Tracking with Motion Modelling for Multiple Object Tracking

ShiJie Sun, Naveed Akhtar, XiangYu Song +3

Deep learning-based Multiple Object Tracking (MOT) currently relies on off-the-shelf detectors for tracking-by-detection.This results in deep models that are detector biased and ev…