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

Associative Learning01:27

Associative Learning

270
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
270
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

93
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
93
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

375
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
375
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

11.6K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
11.6K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

5.2K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
5.2K
Multiple Comparison Tests01:13

Multiple Comparison Tests

3.8K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
3.8K

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

Investigation of Temperature-Field Evolution and Microstructural Response in Bituminous Waterproofing Membranes Under Low-Temperature Flexibility Testing Conditions.

Polymers·2026
Same author

Retraction notice to "Safe use of liver grafts from hepatitis B surface antigen positive donors in liver transplantation" [J Hepatol 61 (2014) 809-815].

Journal of hepatology·2026
Same author

Evolving Strategies for Previously Drugged Therapeutic Targets: Medicinal Chemistry Insights from Paradigmatic Cases.

Journal of medicinal chemistry·2026
Same author

Meckel's diverticulum complicated by a congenital intestinal adhesive band: a case report and literature review.

Frontiers in pediatrics·2026
Same author

Vesicovaginal reflux: a case report and literature overview.

Frontiers in pediatrics·2026
Same author

A highly feasible simulation model based on nipple-preserving pork portions for practicing superficial mass resection.

BMC surgery·2026
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
Same journal

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

IEEE transactions on neural networks and learning systems·2026
Same journal

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

IEEE transactions on neural networks and learning systems·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
查看所有相关文章

相关实验视频

Updated: May 21, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

448

多尺度子图对抗式对比式学习

Yanbei Liu, Yu Zhao, Zhitao Xiao

    IEEE transactions on neural networks and learning systems
    |March 21, 2025
    PubMed
    概括
    此摘要是机器生成的。

    图形对比学习 (GCL) 与多尺度图形结构作斗争. 一种新的多尺度子图对比学习方法 (MSSGCL) 通过考虑不同尺度的语义一致性来改善图表表示.

    更多相关视频

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    340
    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    474

    相关实验视频

    Last Updated: May 21, 2025

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    448
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    340
    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    474

    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 图形神经网络的神经网络

    背景情况:

    • 图形对比学习 (GCL) 是图形表示的一个突出的自我监督学习范式.
    • 现有的GCL方法假定来自同一图的增强视图是正对,这可能会在复杂的多尺度图中失败.
    • 增强图形视图的语义一致性至关重要,但经常被忽视.

    研究的目的:

    • 为了解决标准GCL在处理多尺度图形结构方面的局限性.
    • 提出一种新的GCL方法,通过考虑多尺度图形属性来捕获细粒度的语义信息.
    • 通过先进的技术来提高GCL模型的概括性能.

    主要方法:

    • 开发了一种多尺度子图对比学习 (MSSGCL) 方法,使用全球和本地视图的子图采样.
    • 在不同尺度的语义关联基础上构建多个对比关系.
    • 引入了具有不对称结构的MSSGCL++和对抗性训练,以提高概括性.
    • 优化了min-max点问题,并采用了"免费"策略来更快地训练.

    主要成果:

    • 实验分析显示,语义信息的一致性至关重要,并且取决于图形的多尺度结构.
    • 通过利用多尺度子图视图,MSSGCL有效地表征细粒度的语义信息.
    • 与基础MSSGCL模型相比,MSSGCL++显示了更好的概括性能.
    • 提出的方法在16个现实世界图形分类数据集上实现了与最先进的方法相比的显著改进.

    结论:

    • 标准GCL假设并不总是适用于复杂的多尺度图形.
    • 多尺度子图对比学习为图表表示学习提供了更强大的方法.
    • 拟议的MSSGCL和MSSGCL++方法显著推进了自主监督图形学习领域.
    • 这些发现强调了考虑多个尺度的图形结构对于有效的表示学习的重要性.