32 citations · 97 across the 8 of their papers we have counts for
8 papers
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…
Convolutional Embedding for Edit Distance
Xinyan Dai, Xiao Yan, Kaiwen Zhou +3
Edit-distance-based string similarity search has many applications such as spell correction, data de-duplication, and sequence alignment. However, computing edit distance is known…
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-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search
Xinyan Dai, Xiao Yan, Kelvin K. W. Ng +2
Vector quantization (VQ) techniques are widely used in similarity search for data compression, fast metric computation and etc. Originally designed for Euclidean distance, existing…
Understanding and Improving Proximity Graph based Maximum Inner Product Search
Jie Liu, Xiao Yan, Xinyan Dai +3
The inner-product navigable small world graph (ip-NSW) represents the state-of-the-art method for approximate maximum inner product search (MIPS) and it can achieve an order of mag…
PMD: An Optimal Transportation-based User Distance for Recommender Systems
Yitong Meng, Xinyan Dai, Xiao Yan +5
Collaborative filtering, a widely-used recommendation technique, predicts a user's preference by aggregating the ratings from similar users. As a result, these measures cannot full…
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…