Search Results for author: Xiao Yan

Found 19 papers, 9 papers with code

ProSTformer: Pre-trained Progressive Space-Time Self-attention Model for Traffic Flow Forecasting

no code implementations3 Nov 2021 Xiao Yan, Xianghua Gan, Jingjing Tang, Rui Wang

When pre-trained on the big scale datasets and transferred to the medium and small scale datasets, ProSTformer achieves a significant enhancement and behaves best.

Combating Ambiguity for Hash-code Learning in Medical Instance Retrieval

1 code implementation19 May 2021 Jiansheng Fang, Huazhu Fu, Dan Zeng, Xiao Yan, Yuguang Yan, Jiang Liu

When encountering a dubious diagnostic case, medical instance retrieval can help radiologists make evidence-based diagnoses by finding images containing instances similar to a query case from a large image database.

DGCL: an efficient communication library for distributed GNN training

1 code implementation Proceedings of the Sixteenth European Conference on Computer Systems 2021 Zhenkun Cai, Xiao Yan, Yidi Wu, Kaihao Ma, James Cheng, Fan Yu

Graph neural networks (GNNs) have gained increasing popularity in many areas such as e-commerce, social networks and bio-informatics.

Elastic Deep Learning in Multi-Tenant GPU Clusters

no code implementations IEEE Transactions on Parallel and Distributed Systems 2021 Yidi Wu, Kaihao Ma, Xiao Yan, Zhi Liu, Zhenkun Cai, Yuzhen Huang, James Cheng, Han Yuan, Fan Yu

We study how to support elasticity, that is, the ability to dynamically adjust the parallelism (i. e., the number of GPUs), for deep neural network (DNN) training in a GPU cluster.

Delay-Tolerant Local SGD for Efficient Distributed Training

no code implementations1 Jan 2021 An Xu, Xiao Yan, Hongchang Gao, Heng Huang

The heavy communication for model synchronization is a major bottleneck for scaling up the distributed deep neural network training to many workers.

Federated Learning

The item selection problem for user cold-start recommendation

no code implementations27 Oct 2020 Yitong Meng, Jie Liu, Xiao Yan, James Cheng

When a new user just signs up on a website, we usually have no information about him/her, i. e. no interaction with items, no user profile and no social links with other users.

Recommendation Systems

Demand Forecasting in Bike-sharing Systems Based on A Multiple Spatiotemporal Fusion Network

no code implementations23 Sep 2020 Xiao Yan, Gang Kou, Feng Xiao, Dapeng Zhang, Xianghua Gan

Spatial and temporal features are critical for demand forecasting in BSSs, but it is challenging to extract spatiotemporal dynamics.

Ensemble Learning Feature Importance

TensorOpt: Exploring the Tradeoffs in Distributed DNN Training with Auto-Parallelism

1 code implementation16 Apr 2020 Zhenkun Cai, Kaihao Ma, Xiao Yan, Yidi Wu, Yuzhen Huang, James Cheng, Teng Su, Fan Yu

A good parallelization strategy can significantly improve the efficiency or reduce the cost for the distributed training of deep neural networks (DNNs).

Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs

1 code implementation18 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.

General Classification Node Classification

Convolutional Embedding for Edit Distance

2 code implementations31 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.

Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learning

1 code implementation12 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.

Federated Learning Quantization

Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search

2 code implementations12 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.

Data Compression Quantization

Understanding and Improving Proximity Graph based Maximum Inner Product Search

no code implementations30 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.

PMD: An Optimal Transportation-based User Distance for Recommender Systems

no code implementations10 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.

Collaborative Filtering Recommendation Systems

Norm-Range Partition: A Universal Catalyst for LSH based Maximum Inner Product Search (MIPS)

1 code implementation22 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.

Norm-Ranging LSH for Maximum Inner Product Search

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.

Beyond Precision: A Study on Recall of Initial Retrieval with Neural Representations

no code implementations22 Jul 2018 Xiao Yan, Guo Jiafeng, Fan Yixing, Lan Yanyan, Xu Jun, Cheng Xueqi

Our experiments show that both hybrid index and search schemes can improve the recall of the initial retrieval stage with small overhead.

Information Retrieval Re-Ranking

Cannot find the paper you are looking for? You can Submit a new open access paper.