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

605
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...
605
Observational Learning01:12

Observational Learning

321
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
321
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
Introduction to Learning01:18

Introduction to Learning

551
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
551
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.3K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.3K
Causes of Similarity-Dissimilarity Effect01:26

Causes of Similarity-Dissimilarity Effect

2
The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...
2

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

Interpretable agentic AI system with localized reasoning for radiology.

NPJ digital medicine·2026
Same author

SymBOL: A General-Purpose Symbolic Learner for Scientific Discovery Using Bayesian Optimization-Enhanced Large Language Models.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing and Evaluating Multimodal Large Language Models in Ophthalmology.

ACM transactions on computing for healthcare·2026
Same author

Contrastive multi-view representation learning for multi-camera plant phenotyping: A cotton field study.

Plant phenomics (Washington, D.C.)·2026
Same author

Raising the Bar in Graph OOD Generalization: Invariant Learning beyond Explicit Environment Modeling.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Impact of Fostamatinib on Inflammatory Biomarkers in Hospitalized Patients With COVID-19.

Critical care explorations·2026

相关实验视频

Updated: Sep 19, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

595

AdaGCL+:一个自适应的子图对比学习,以解决拓学偏见.

Yili Wang, Yaohua Liu, Ninghao Liu

    IEEE transactions on pattern analysis and machine intelligence
    |June 5, 2025
    PubMed
    概括

    自适应子图对比学习 (AdaGCL+) 解决了图形神经网络 (GNN) 培训可扩展性的挑战. 它通过学习节点嵌入在增强子图中不变的改进了概括,优于现有的方法.

    科学领域:

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

    背景情况:

    • 在大规模图形数据上训练图形神经网络 (GNN) 面临着可扩展性挑战.
    • 用于减轻可扩展性问题的批量采样方法引入拓偏差,对GNN概括产生负面影响.
    • 这种偏差来自于不完整的子图结构,与完整的图相比缺失的节点特征或边缘.

    研究的目的:

    • 提出自适应子图对比学习 (AdaGCL),以弥合批量采样和GNN概括之间的差距.
    • 开发一个增强版本,AdaGCL+,可以自动增强图形,以改进节点嵌入.
    • 优化下游任务的增强策略,使用以节点为中心的信息瓶 (Node-IB).

    主要方法:

    • AdaGCL扩大了采样子图,并使用对比损失来学习不变节点嵌入.
    • 引入了Node-IB以控制图形增强中的相似性和多样性之间的权衡.
    • AdaGCL+动态调整图形扰动参数,以最大限度地减少下游损失,自动增强.

    主要成果:

    • 通过批量采样,AdaGCL+证明了对具有数百万节点的图形的可扩展性.
    • 该方法始终优于对基准数据集的现有方法,以确定节点分类的准确性.

    更多相关视频

    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

    651
    Portable Intermodal Preferential Looking IPL: Investigating Language Comprehension in Typically Developing Toddlers and Young Children with Autism
    10:11

    Portable Intermodal Preferential Looking IPL: Investigating Language Comprehension in Typically Developing Toddlers and Young Children with Autism

    Published on: December 14, 2012

    18.6K

    相关实验视频

    Last Updated: Sep 19, 2025

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
    05:47

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

    Published on: June 13, 2025

    595
    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

    651
    Portable Intermodal Preferential Looking IPL: Investigating Language Comprehension in Typically Developing Toddlers and Young Children with Autism
    10:11

    Portable Intermodal Preferential Looking IPL: Investigating Language Comprehension in Typically Developing Toddlers and Young Children with Autism

    Published on: December 14, 2012

    18.6K
  • 与以前的方法相比,AdaGCL+显示出更高的运行时间效率.
  • 结论:

    • AdaGCL+有效地解决了大型GNN培训中的拓偏差和泛化问题.
    • 自动化图表增强策略提高了GNN的性能和效率.
    • AdaGCL+提供了一个可扩展和有效的解决方案,用于在巨大的图形数据集上训练GNN.