To address these issues, we propose an explainable spatio-temporal graph evolution learning (ESTGEL) model to model the dynamic evolution of INRs. Specifically, an edge attention module is proposed to utilize the spatial neighborhood of an INR at multi-level, i.e., a hierarchy of nested ...
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 巴拉巴拉 积极向上的懒人 1.创新点 第一篇将图卷积用于提取空间和时间信息的文章,没有使用正则卷积和递归单元,使用了完整的卷积结构,在更少的参数下,可以得到更快的训练速度 2.问题描述 通过前[t-m+1,t] 的交通流...
Spatio-Temporal Meta-Graph Learning for Traffc Forecasting, 视频播放量 186、弹幕量 0、点赞数 2、投硬币枚数 2、收藏人数 3、转发人数 2, 视频作者 机智小赛尔, 作者简介 ,相关视频:SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivaria
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 论文笔记---GCN在交通领域的应用(二) 一、论文翻译: 1、摘要: 及时准确地交通预测对于城市交通控制和指导具有至关重要的意义。由于交通流量的非线... 查看...
Spatio-Temporal Graph Convolutional Networks A Deep Learning Framework for Traffic Forecasting,程序员大本营,技术文章内容聚合第一站。
Spatial: graph, Temporal: Conv Spatio-temporal convolutional networks 2 Preliminary 2.1 Traffic Prediction on Road Graphs Traffic forecast: v^t+1,…,v^t+H=argmaxvt+1,…,vt+HlogP(vt+1,…,vt+H∣vt−M+1,…,vt) Gt=(Vt,E,W) ...
Fig. 1. Schematic architecture of spatio-temporal directed acyclic graph learning framework with attention mechanisms (ST-DAG-Att). Abbreviations: ST-graph-conv, spatio-temporal graph convolution; ST-aggregation, spatio-temporal aggregation; FC-conv, functional connectivity convolution; FC-SAtt, functiona...
we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolut...
To address the spatio-temporal heterogeneity and non-stationarity implied in the traffic stream, in this study, we propose Spatio-Temporal Meta-Graph Learning as a novel Graph Structure Learning mechanism on spatio-temporal data. Specifically, we implement this idea into Meta-Graph Convolutional ...
加权求和，指导结构感知学习。ST-Meta Graph Reconstruction预测边可能性，指导元知识重构，引入图重建损失优化。参数生成方法，节点元知识指导模型参数生成，减少参数量，适应不同场景。ST-GFSL学习过程遵循MAML的episode学习，从源数据集中采样训练任务，优化模型适应能力，实现小样本场景的高效学习。