2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2025
Efficiency Robustness of Dynamic Deep Learning Systems
Ravishka Rathnasuriya, Tingxi Li, Zexin Xu +4
Deep Learning Systems (DLSs) are increasingly deployed in real-time applications, including those in resourceconstrained environments such as mobile and IoT devices. To address eff…
cs.LG2024
Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation
Weizhi Zhang, Liangwei Yang, Zihe Song +4
The efficiency and scalability of graph convolution networks (GCNs) in training recommender systems (RecSys) have been persistent concerns, hindering their deployment in real-world…
cs.IR2024★ 2 cited
Mixed Supervised Graph Contrastive Learning for Recommendation
Weizhi Zhang, Liangwei Yang, Zihe Song +4
Recommender systems (RecSys) play a vital role in online platforms, offering users personalized suggestions amidst vast information. Graph contrastive learning aims to learn from h…