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

1.2K
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...
1.2K
Observational Learning01:12

Observational Learning

832
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...
832
Cognitive Learning01:21

Cognitive Learning

1.0K
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
1.0K
Gradient and Del Operator01:14

Gradient and Del Operator

4.3K
In mathematics and physics, the gradient and del operator are fundamental concepts used to describe the behavior of functions and fields in space. The gradient is a mathematical operator that gives both the magnitude and direction of the maximum spatial rate of change. Consider a person standing on a mountain. The slope of the mountain at any given point is not defined unless it is quantified in a particular direction. For this reason, a "directional derivative" is defined, which is a vector...
4.3K
Introduction to Learning01:18

Introduction to Learning

945
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...
945
Reducing Line Loss01:18

Reducing Line Loss

360
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
360

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

Longitudinal Association Between Possible Sarcopenia and Stroke Under the AWGS 2025 Criteria: A Nationwide Prospective Cohort Study With a 9-Year Follow-Up.

Geriatrics & gerontology international·2026
Same author

Audiogram Configuration Predicts Treatment Response in Sudden Sensorineural Hearing Loss.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery·2026
Same author

Temperature-responsive PtFe nanowire peroxidase mimetic array for colorimetric discrimination of biogenic amines.

Talanta·2026
Same author

Angiodysplasia as a rare cause of acute hematochezia in a 33-year-old with vascular risk factors: a case report.

Frontiers in medicine·2026
Same author

Sulfur-Doped High-Entropy Spinel Oxide (FeCoNiCuCrAlZn)<sub>3</sub>O<sub>4</sub> Electrocatalyst for Seawater Electrolysis.

ChemSusChem·2026
Same author

Research based on nucleotide polymorphism reveals the role of inflammatory cytokines in regulating the influence of blood metabolites on drug-related osteonecrosis.

Archives of medical science : AMS·2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

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

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

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

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

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

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

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

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

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

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026
查看所有相关文章

相关实验视频

Updated: Jan 15, 2026

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

1.0K

基于图形的层-适应-增大对比学习与特征脱相关性.

Yuhua Xu, Junli Wang, Rui Duan

    IEEE transactions on pattern analysis and machine intelligence
    |October 7, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了基于层适应增强的图形对比学习与特征脱相关性 (LGCLD),以增强图形表示学习. LGCLD提高了模型的稳定性,并减少了功能冗余,以在标签稀缺场景中获得更好的性能.

    更多相关视频

    Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
    07:12

    Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

    Published on: April 11, 2025

    876

    相关实验视频

    Last Updated: Jan 15, 2026

    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

    1.0K
    Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
    07:12

    Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

    Published on: April 11, 2025

    876

    科学领域:

    • 机器学习 机器学习
    • 图形表示学习学习学习图形表示学习
    • 人工智能的人工智能

    背景情况:

    • 图形对比学习 (GCL) 方法对于在标签稀缺的场景中学习表征至关重要.
    • 现有的GCL方法在自适应增强和图内特征冗余方面存在局限性.
    • 低于最佳的模型稳定性和功能冗余性阻碍了GCL的性能.

    研究的目的:

    • 提出一个新的图形对比学习框架,LGCLD,解决现有方法的局限性.
    • 通过层层的自适应增强来增强模型的强度.
    • 通过优化图间协议和图内特征关系来学习信息和非冗余的图表表示.

    主要方法:

    • 为动态,语义上相似的图形扰动开发了一种层wise适应增强技术.
    • 引入了协议-脱相关性 (AD) 损失,以优化图表级别表示协议和特征脱相关性.
    • 使用图形信息瓶原理分析了AD损失.

    主要成果:

    • 通过自适应增强,LGCLD显示了改进的模型稳定性.
    • 在图形表示中,AD损失有效地减少了维特征冗余.
    • 实验表明,LGCLD在各种图形数据集中实现了竞争性或优异的性能.

    结论:

    • LGCLD为图形对比学习提供了一种强大而有效的方法.
    • 提出的方法解决了增强和表示学习的关键局限性.
    • LGCLD推进了对标签稀缺任务的图形表示学习的最先进技术.