80 citations · 290 across the 20 of their papers we have counts for
20 papers
Being Automated or Not? Risk Identification of Occupations with Graph Neural Networks
Dawei Xu, Haoran Yang, Marian-Andrei Rizoiu +1
The rapid advances in automation technologies, such as artificial intelligence (AI) and robotics, pose an increasing risk of automation for occupations, with a likely significant i…
On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation
Xin Xia, Hongzhi Yin, Junliang Yu +3
Modern recommender systems operate in a fully server-based fashion. To cater to millions of users, the frequent model maintaining and the high-speed processing for concurrent user…
Dual Space Graph Contrastive Learning
Haoran Yang, Hongxu Chen, Shirui Pan +3
Unsupervised graph representation learning has emerged as a powerful tool to address real-world problems and achieves huge success in the graph learning domain. Graph contrastive l…
What Do They Capture? -- A Structural Analysis of Pre-Trained Language Models for Source Code
Yao Wan, Wei Zhao, Hongyu Zhang +3
Recently, many pre-trained language models for source code have been proposed to model the context of code and serve as a basis for downstream code intelligence tasks such as code…
Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation
Xiangmeng Wang, Qian Li, Dianer Yu +3
Traditional recommendation models trained on observational interaction data have generated large impacts in a wide range of applications, it faces bias problems that cover users' t…
Graph Masked Autoencoders with Transformers
Sixiao Zhang, Hongxu Chen, Haoran Yang +3
Recently, transformers have shown promising performance in learning graph representations. However, there are still some challenges when applying transformers to real-world scenari…