1 code implementation • NeurIPS 2018 • Xiao Yan, Jinfeng Li, Xinyan Dai, Hongzhi Chen, James Cheng
Neyshabur and Srebro proposed Simple-LSH, which is the state-of-the-art hashing method for maximum inner product search (MIPS) with performance guarantee.
1 code implementation • 22 Oct 2018 • Xiao Yan, Xinyan Dai, Jie Liu, Kaiwen Zhou, James Cheng
Recently, locality sensitive hashing (LSH) was shown to be effective for MIPS and several algorithms including $L_2$-ALSH, Sign-ALSH and Simple-LSH have been proposed.
no code implementations • 10 Sep 2019 • Yitong Meng, Xinyan Dai, Xiao Yan, James Cheng, Weiwen Liu, Benben Liao, Jun Guo, Guangyong Chen
Collaborative filtering, a widely-used recommendation technique, predicts a user's preference by aggregating the ratings from similar users.
no code implementations • 30 Sep 2019 • Jie Liu, Xiao Yan, Xinyan Dai, Zhirong Li, James Cheng, Ming-Chang Yang
Then we explain the good performance of ip-NSW as matching the norm bias of the MIPS problem - large norm items have big in-degrees in the ip-NSW proximity graph and a walk on the graph spends the majority of computation on these items, thus effectively avoids unnecessary computation on small norm items.
2 code implementations • 12 Nov 2019 • Xinyan Dai, Xiao Yan, Kelvin K. W. Ng, Jie Liu, James Cheng
In this paper, we present a new angle to analyze the quantization error, which decomposes the quantization error into norm error and direction error.
1 code implementation • 12 Nov 2019 • Xinyan Dai, Xiao Yan, Kaiwen Zhou, Han Yang, Kelvin K. W. Ng, James Cheng, Yu Fan
In particular, at the high compression ratio end, HSQ provides a low per-iteration communication cost of $O(\log d)$, which is favorable for federated learning.
2 code implementations • 31 Jan 2020 • Xinyan Dai, Xiao Yan, Kaiwen Zhou, Yuxuan Wang, Han Yang, James Cheng
Edit-distance-based string similarity search has many applications such as spell correction, data de-duplication, and sequence alignment.
1 code implementation • 18 Feb 2020 • Han Yang, Xiao Yan, Xinyan Dai, Yongqiang Chen, James Cheng
In this paper, we propose self-enhanced GNN (SEG), which improves the quality of the input data using the outputs of existing GNN models for better performance on semi-supervised node classification.