Search Results for author: Dongrui Fan

Found 13 papers, 5 papers with code

A Comprehensive Survey on Distributed Training of Graph Neural Networks

no code implementations10 Nov 2022 Haiyang Lin, Mingyu Yan, Xiaochun Ye, Dongrui Fan, Shirui Pan, WenGuang Chen, Yuan Xie

In addition, their computational patterns and communication patterns, as well as the optimization techniques proposed by recent work are introduced.

Rethinking Efficiency and Redundancy in Training Large-scale Graphs

no code implementations2 Sep 2022 Xin Liu, Xunbin Xiong, Mingyu Yan, Runzhen Xue, Shirui Pan, Xiaochun Ye, Dongrui Fan

Thereby, we propose to drop redundancy and improve efficiency of training large-scale graphs with GNNs, by rethinking the inherent characteristics in a graph.

Simple and Efficient Heterogeneous Graph Neural Network

2 code implementations6 Jul 2022 Xiaocheng Yang, Mingyu Yan, Shirui Pan, Xiaochun Ye, Dongrui Fan

Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations.

Node Property Prediction

Characterizing and Understanding Distributed GNN Training on GPUs

no code implementations18 Apr 2022 Haiyang Lin, Mingyu Yan, Xiaocheng Yang, Mo Zou, WenMing Li, Xiaochun Ye, Dongrui Fan

Graph neural network (GNN) has been demonstrated to be a powerful model in many domains for its effectiveness in learning over graphs.

Survey on Graph Neural Network Acceleration: An Algorithmic Perspective

no code implementations10 Feb 2022 Xin Liu, Mingyu Yan, Lei Deng, Guoqi Li, Xiaochun Ye, Dongrui Fan, Shirui Pan, Yuan Xie

Next, we provide comparisons from aspects of the efficiency and characteristics of these methods.

GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware

1 code implementation26 Aug 2021 Xin Liu, Mingyu Yan, Shuhan Song, Zhengyang Lv, WenMing Li, Guangyu Sun, Xiaochun Ye, Dongrui Fan

Extensive experiments show that our method is universal to mainstream sampling algorithms and helps significantly reduce the training time, especially in large-scale graphs.

Tackling Variabilities in Autonomous Driving

no code implementations21 Apr 2021 Yuqiong Qi, Yang Hu, Haibin Wu, Shen Li, Haiyu Mao, Xiaochun Ye, Dongrui Fan, Ninghui Sun

In this work, we aim to extensively explore the above system design challenges and these challenges motivate us to propose a comprehensive framework that synergistically handles the heterogeneous hardware accelerator design principles, system design criteria, and task scheduling mechanism.

Autonomous Driving Reinforcement Learning (RL) +1

Sampling methods for efficient training of graph convolutional networks: A survey

no code implementations10 Mar 2021 Xin Liu, Mingyu Yan, Lei Deng, Guoqi Li, Xiaochun Ye, Dongrui Fan

Graph Convolutional Networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations.

Video Face Recognition System: RetinaFace-mnet-faster and Secondary Search

1 code implementation28 Sep 2020 Qian Li, Nan Guo, Xiaochun Ye, Dongrui Fan, Zhimin Tang

Ours is suitable for large-scale datasets, and experimental results show that our method is 82% faster than the violent retrieval for the single-frame detection.

Face Recognition Retrieval

Top-Related Meta-Learning Method for Few-Shot Object Detection

1 code implementation14 Jul 2020 Qian Li, Nan Guo, Xiaochun Ye, Duo Wang, Dongrui Fan, Zhimin Tang

Therefore, based on semantic features, we propose a Top-C classification loss (i. e., TCL-C) for classification task and a category-based grouping mechanism for category-based meta-features obtained by the meta-model.

Few-Shot Object Detection Meta-Learning +1

HyGCN: A GCN Accelerator with Hybrid Architecture

1 code implementation7 Jan 2020 Mingyu Yan, Lei Deng, Xing Hu, Ling Liang, Yujing Feng, Xiaochun Ye, Zhimin Zhang, Dongrui Fan, Yuan Xie

In this work, we first characterize the hybrid execution patterns of GCNs on Intel Xeon CPU.

Distributed, Parallel, and Cluster Computing

Utilizing the Instability in Weakly Supervised Object Detection

no code implementations14 Jun 2019 Yan Gao, Boxiao Liu, Nan Guo, Xiaochun Ye, Fang Wan, Haihang You, Dongrui Fan

Weakly supervised object detection (WSOD) focuses on training object detector with only image-level annotations, and is challenging due to the gap between the supervision and the objective.

Multiple Instance Learning object-detection +1

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