2 citations · 5 across the 5 of their papers we have counts for
3 papers
cs.LG2023★ 1 cited
Generative Flow Networks for Precise Reward-Oriented Active Learning on Graphs
Yinchuan Li, Zhigang Li, Wenqian Li +3
Many score-based active learning methods have been successfully applied to graph-structured data, aiming to reduce the number of labels and achieve better performance of graph neur…
cs.DC2022★ 1 cited
Energy Savings When Migrating Workloads to the Cloud
Yan Zheng, Stephan Bohacek
In the cloud environment, data centers are efficiently manipulated by cloud service providers (CSPs) in terms of energy consumption. Consequently, migrating workloads to clouds can…
cs.LG2022
Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph
Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng +6
Graph neural networks (GNNs) are deep learning models designed specifically for graph data, and they typically rely on node features as the input to the first layer. When applying…