文章目录 摘要 1 简介 1.1 GNN简史 1.2 Related surveys on graph neural networks 1.3 Graph neural networks vs. network embedding 1.4 Graph neural networks vs. graph kernel methods 1.5 文章的创新性 2 基本的图概念的定义 3 GNN分类和框架... 查看原文 005 CNN、GNN DNN(Convolutional Neural Network,...
Model Compression and Hardware Acceleration for Neural Networks A Comprehensive Survey 神经网络的模型压缩和硬件加速:综述 摘要 由于摩尔定律的可预见的终结,在通用处理器的改进速度下降的背景下,特定领域的硬件正成为一个有前途的话题。机器学习,尤其是深度神经网络(DNN),因为其在各种人工智能(AI)任务中的成功应用...
Graph Neural Networks Interpretability and Analysis Meta Learning Metric Learning ML Applications Model Compression and Acceleration Multi-Task and Multi-View Learning Online Learning Optimization Semi-Supervised and Unsupervised Learning Transfer Learning Trustworthy Machine Learning To reduce class imbalance, we...
Engineering, University of Science and Technology Beijing, Beijing 100083, Chinab Hua Xia General Processor Technologies, Beijing 100080, Chinac General Processor Technologies, Tarrytown, NY 10591, United StatesARTICLE INFOKeywords:convolutional neural networkneural network accelerationneural network quantiza....
Graph Neural Networks Interpretability and Analysis Meta Learning Metric Learning ML Applications Model Compression and Acceleration Multi-Task and Multi-View Learning Online Learning Optimization Semi-Supervised and Unsupervised Learning Transfer Learning Trustworthy Machine Learning To reduce class imbalance, we...
A Survey of Model Compression and Acceleration for Deep Neural Network时s 本文全面概述了深度神经网络的压缩方法,主要可分为参数修剪与共享、低秩分解、迁移/压缩卷积滤波器和知识精炼,论文对每一类方法的性能、相关应用、优势和缺陷等方面进行了独到分析。
Neural network accelerationNeural network quantizationNeural network pruningLow-bit mathematicsDeep neural networks have been applied in many applications exhibiting extraordinary abilities in the field of computer vision. However, complex network architectures challenge efficient real-time deployment and require ...
EvGNN: An Event-driven Graph Neural Network Accelerator for Edge Vision promising solution for sparse event-based vision, their irregular structure is a challenge that currently hinders the design of efficient hardware accelerators... Yang, Yufeng,Kneip, Adrian,Frenkel, Charlotte 被引量: 0发表: 202...
Exploratory Adversarial Attacks on Graph Neural Networks 依赖training loss的最大梯度的这种基于梯度的策略,在攻击GNN模型时候,可能不会产生一个好的结果。 原因在于图结构的离散的特点。 ⇓ \Downarrow ⇓ 我们可不可以推导出一种有效的方法,来选择攻击GNN的扰动? 我们提出一种新颖的explorat... ...
A Survey on Graph Neural Networks for Knowledge Graph Completion.arXiv 2020paperbib Siddhant Arora A Survey on Knowledge Graphs: Representation, Acquisition and Applications.arXiv 2020paperbib Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, Philip S. Yu ...