Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Survival Tree01:19

Survival Tree

166
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
166
Time-Series Graph00:54

Time-Series Graph

4.5K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.5K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Federated orthogonal learning for detection of liver lesions from multi-phase contrast-enhanced CT images.

NPJ digital medicine·2026
Same author

STFANet: A spatial and temporal feature aggregation network for fake face detection in videos.

PloS one·2025
Same author

CSTSINR: improving temporal continuity via convolutional structured implicit neural representations for time series anomaly detection.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Contrastive learning unlocks geometric insights for dataset pruning.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

A lightweight fabric defect detection with parallel dilated convolution and dual attention mechanism.

PeerJ. Computer science·2025
Same author

Contrastive Federated Learning for Graph Anomaly Detection.

IEEE transactions on neural networks and learning systems·2025
Same journal

A boundary-regularization-enhanced video anomaly detection network based on context-adaptive spatio-temporal conditional diffusion.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

MT<sup>2</sup>-CSD and LLM-CRAN: A new dataset and an LLM-based multi-semantic knowledge fusion model for conversational stance detection.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

TriAlignNet: A triple-path cross-modality alignment framework for multimodal time series forecasting.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Anchor-based disentanglement framework for incremental multi-view clustering.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Complex-valued amplitude-phase interference modeling for adversarially robust classification.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

TraNce: Type-aware hypergraph neural network with biological mediators for drug repositioning.

Neural networks : the official journal of the International Neural Network Society·2026
查看所有相关文章

相关实验视频

Updated: Sep 19, 2025

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.6K

无监督图形异常检测频率自适应图形神经网络

Ming Gu1, Gaoming Yang2, Zhuonan Zheng1

  • 1College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China.

Neural networks : the official journal of the International Neural Network Society
|May 31, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了用于无监督图形异常检测 (FAGAD) 的频率自适应图形神经网络. 通过适应性地将信号跨频率融合,FAGAD有效地识别图形异常,在没有标记数据的情况下获得最先进的结果.

关键词:
图形神经网络 图形神经网络图形过的过方法无监督的图形异常检测检测

更多相关视频

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.3K

相关实验视频

Last Updated: Sep 19, 2025

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.6K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.3K

科学领域:

  • 图形神经网络 图形神经网络
  • 机器学习 机器学习
  • 数据挖掘 数据挖掘

背景情况:

  • 无监督图形异常检测 (UGAD) 方法通常依赖图形神经网络 (GNN) 过低频图形信号.
  • 异常可以将图形信号频率转移到更高的频段,违反GNN假设并阻碍检测.
  • 现有的先进的图形过器通常需要异常标签,从而限制了它们在现实世界中的适用性.

研究的目的:

  • 解决目前无监督图形异常检测方法的局限性.
  • 提出一种新的方法,以无监督的方式设计有效的图形过器.
  • 开发一个图形神经网络,能够处理由异常引起的频率转移.

主要方法:

  • 提出频率自我适应图形神经网络 (FAGAD).
  • 通过使用全通信号作为参考,在多个频段中自适应地融合图形信号.
  • 通过自我监督的学习方法优化模型,用于表示生成.

主要成果:

  • 在异常检测任务上,FAGAD展示了最先进的性能.
  • 该方法在人工生成的和真实世界的数据集上都实现了高精度.
  • 拟议的方法有效地处理了图形信号频率转移所带来的挑战.

结论:

  • FAGAD提供了一个强大的解决方案,用于无监督的图形异常检测.
  • 自主监督学习框架使有效的表现学习能够在没有标记数据的情况下实现.
  • 多频信号的自适应融合是FAGAD卓越性能的关键.