Interpretable Spatial|Temporal Graph Convolutional Network for ...

Interpretable Spatial–Temporal Graph Convolutional Network for ...

Interpretable Spatial–Temporal Graph Convolutional Network for System Log Anomaly Detection ; Видання: Advanced Engineering Informatics, 2024, с. 102803.

What is Spatial-Temporal Graph Convolutional Networks (ST-GCN)

Spatial-Temporal Graph Convolutional Networks (ST-GCN) enable deep learning on graph-structured data, capturing complex relationships and patterns in ...

Spatial Temporal Graph CNNs for Skeleton-Based Action Recognition

3.3 Spatial Graph Convolutional Neural Network. Before we dive into the ... Interpretable 3d human action analysis with temporal convolutional networks.

Graph Convolutional Network-Based Interpretable Machine ...

... network (GCN) to incorporate these features with topology information for SVS assessment. The GCN explores the spatial-temporal dynamics of ...

Forecasting using spatio-temporal data with combined Graph ...

Interpretability of node classification results ... The architecture of the GCN-LSTM model is inspired by the paper: T-GCN: A Temporal Graph Convolutional Network ...

Spatio-Temporal Graph Convolutional Networks: A Deep Learning ...

Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. Bing Yu, Haoteng Yin, Zhanxing Zhu. Proceedings of the Twenty ...

[PDF] A spatial–temporal graph deep learning model for urban flood ...

Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting ... Interpretable spatio-temporal attention LSTM ...

STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional ...

... interpretability and robustness of the prediction model are addressed ... dynamic spatial–temporal graph convolutional neural network (DGCNN). On the ...

Attention Based Spatial-Temporal Graph Convolutional Networks for ...

In this paper, we propose a novel attention based spatial-temporal graph con- volutional network (ASTGCN) model to solve traffic flow forecasting problem.

AI Papers on X: "Context-based Interpretable Spatio-Temporal ...

Context-based Interpretable Spatio-Temporal Graph Convolutional Network for Human Motion Forecasting. https://t.co/H7nr7W90uU.

Interpretable causal-based temporal graph convolutional network ...

Interpretable causal-based temporal graph convolutional network framework in complex spatio-temporal systems for CCUS-EOR. Author & abstract; Download; Related ...

Combining graph neural networks and spatio-temporal disease ...

In this context, reliable and interpretable forecasts of disease incidents are a vital tool for policymakers to manage healthcare resources. In ...

An attentional spatial temporal graph convolutional network with co ...

In this work, we design a multi-task framework that improves the recent Spatial-Temporal Graph Convolutional Networks (ST-GCN) for skeleton-based action ...

Search for ST-GCN | Papers With Code

This work leverages novel spatial-temporal graph convolutional network (ST-GCN) architectures and training procedures to predict clinical scores of ...

Temporal Graph Convolutional Networks for Automatic Seizure ...

Additionally, we investigate interpretability advantages of TGCN by exploring approaches for helping clinicians determine when precisely seizures occur, and ...

Spatial-temporal graph neural ODE networks for skeleton-based ...

It is worth mentioning that the dynamics introduced by the ODE-TCN module improve the interpretability of the model in this task domain. Second, ...

Spatial-temporal dual-channel adaptive graph convolutional ... - OUCI

Spatial-temporal dual-channel adaptive graph convolutional network for remaining useful life prediction with multi-sensor information fusion ... Authors: Xingwu ...

Spatial-Temporal Attention Mechanism and Graph Convolutional ...

The above neural network learning methods for destination prediction ignore the important feature of spatial-temporal correlation, and the need ...

Temporal Graph Convolutional Networks for Automatic Seizure ...

Additionally, we investigate interpretability advantages of TGCN by exploring approaches for helping clinicians determine when precisely seizures occur, and the ...

An interpretable graph convolutional neural network based fault ...

This study proposed a fault diagnosis method based on interpretable graph neural network (GNN) suitable for building energy systems.