9 citations · 21 across the 7 of their papers we have counts for
7 papers
Brain Storm Optimization Based Swarm Learning for Diabetic Retinopathy Image Classification
Liang Qu, Cunze Wang, Yuhui Shi
The application of deep learning techniques to medical problems has garnered widespread research interest in recent years, such as applying convolutional neural networks to medical…
HeteFedRec: Federated Recommender Systems with Model Heterogeneity
Wei Yuan, Liang Qu, Lizhen Cui +3
Owing to the nature of privacy protection, federated recommender systems (FedRecs) have garnered increasing interest in the realm of on-device recommender systems. However, most ex…
Personalized Elastic Embedding Learning for On-Device Recommendation
Ruiqi Zheng, Liang Qu, Tong Chen +3
To address privacy concerns and reduce network latency, there has been a recent trend of compressing cumbersome recommendation models trained on the cloud and deploying compact rec…
Semi-decentralized Federated Ego Graph Learning for Recommendation
Liang Qu, Ningzhi Tang, Ruiqi Zheng +4
Collaborative filtering (CF) based recommender systems are typically trained based on personal interaction data (e.g., clicks and purchases) that could be naturally represented as…
Single-shot Embedding Dimension Search in Recommender System
Liang Qu, Yonghong Ye, Ningzhi Tang +3
As a crucial component of most modern deep recommender systems, feature embedding maps high-dimensional sparse user/item features into low-dimensional dense embeddings. However, th…
AutoML for Deep Recommender Systems: A Survey
Ruiqi Zheng, Liang Qu, Bin Cui +2
Recommender systems play a significant role in information filtering and have been utilized in different scenarios, such as e-commerce and social media. With the prosperity of deep…
ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks
Liang Qu, Huaisheng Zhu, Ruiqi Zheng +2
Imbalanced classification on graphs is ubiquitous yet challenging in many real-world applications, such as fraudulent node detection. Recently, graph neural networks (GNNs) have sh…