62 citations · 168 across the 9 of their papers we have counts for
9 papers
From Occupations to Tasks: A New Perspective on Automatability Prediction Using BERT
Dawei Xu, Haoran Yang, Marian-Andrei Rizoiu +1
As automation technologies continue to advance at an unprecedented rate, concerns about job displacement and the future of work have become increasingly prevalent. While existing r…
LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning
Haoran Yang, Xiangyu Zhao, Sirui Huang +2
Graph Contrastive Learning (GCL) is a potent paradigm for self-supervised graph learning that has attracted attention across various application scenarios. However, GCL for learnin…
Torpor: GPU-Enabled Serverless Computing for Low-Latency, Resource-Efficient Inference
Minchen Yu, Ao Wang, Dong Chen +8
Serverless computing offers a compelling cloud model for online inference services. However, existing serverless platforms lack efficient support for GPUs, hindering their ability…
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…
Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning
Haoran Yang, Xiangyu Zhao, Muyang Li +2
Currently, graph learning models are indispensable tools to help researchers explore graph-structured data. In academia, using sufficient training data to optimize a graph model on…
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning
Haoran Yang, Hongxu Chen, Sixiao Zhang +4
Graph contrastive learning has emerged as a powerful tool for unsupervised graph representation learning. The key to the success of graph contrastive learning is to acquire high-qu…
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…
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…
Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation
Haoran Yang, Hongxu Chen, Lin Li +2
User purchasing prediction with multi-behavior information remains a challenging problem for current recommendation systems. Various methods have been proposed to address it via le…