activity
20172021
most citedInformer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

476 citations · 615 across the 14 of their papers we have counts for

collaborators

22 papers

cs.CY20219 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.LG20212 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.CL2021

RoSearch: Search for Robust Student Architectures When Distilling Pre-trained Language Models

Xin Guo, Jianlei Yang, Haoyi Zhou +2

Pre-trained language models achieve outstanding performance in NLP tasks. Various knowledge distillation methods have been proposed to reduce the heavy computation and storage requ…

cs.LG20213 cited

A Robust and Generalized Framework for Adversarial Graph Embedding

Jianxin Li, Xingcheng Fu, Hao Peng +5

Graph embedding is essential for graph mining tasks. With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-qua…

cs.LG2021

Differentially Private Federated Knowledge Graphs Embedding

Hao Peng, Haoran Li, Yangqiu Song +2

Knowledge graph embedding plays an important role in knowledge representation, reasoning, and data mining applications. However, for multiple cross-domain knowledge graphs, state-o…