32 citations · 97 across the 8 of their papers we have counts for
4 papers · 1 filter
Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs
Han Yang, Xiao Yan, Xinyan Dai +2
Graph neural networks (GNNs) have received much attention recently because of their excellent performance on graph-based tasks. However, existing research on GNNs focuses on design…
Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learning
Xinyan Dai, Xiao Yan, Kaiwen Zhou +4
The high cost of communicating gradients is a major bottleneck for federated learning, as the bandwidth of the participating user devices is limited. Existing gradient compression…
Norm-Range Partition: A Universal Catalyst for LSH based Maximum Inner Product Search (MIPS)
Xiao Yan, Xinyan Dai, Jie Liu +2
Recently, locality sensitive hashing (LSH) was shown to be effective for MIPS and several algorithms including -ALSH, Sign-ALSH and Simple-LSH have been proposed. In this pape…
Norm-Ranging LSH for Maximum Inner Product Search
Xiao Yan, Jinfeng Li, Xinyan Dai +2
Neyshabur and Srebro proposed Simple-LSH, which is the state-of-the-art hashing method for maximum inner product search (MIPS) with performance guarantee. We found that the perform…