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

276
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...
276
Improving Translational Accuracy02:07

Improving Translational Accuracy

2.5K
2.5K
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

399
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...
399
Aggregates Classification01:29

Aggregates Classification

298
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
298
Ogive Graph01:07

Ogive Graph

5.5K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
5.5K
Long-term Potentiation01:35

Long-term Potentiation

54.6K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
54.6K

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

Hybrid evolutionary-gradient training improves long-term time series forecasting.

Scientific reports·2026
Same author

Revealing the activation mechanism of periodate by groundwater treatment plant waste iron-containing sludge for sulfadiazine removal: the key activation role of transition metal Mn.

Environmental research·2026
Same author

Nitrosocosmicus AOA-driven anammox bacteria enrichment in counter-current aerated biofilters: Unraveling the start-up mechanism of the novel autotrophic nitrification-denitrification process.

Bioresource technology·2025
Same author

SLC16A1 Inhibits Ferroptosis and Promotes the Progression of Head and Neck Squamous Cell Carcinoma.

Journal of Cancer·2025
Same author

Enrichment of Nitrosocosmicus-AOA in situ and their vertical distribution characteristics in aerated biofilters.

Environmental research·2025
Same author

Genomic Insights into Post-Domestication Expansion and Selection of Body Size in Ponies.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025

相关实验视频

Updated: May 24, 2025

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

475

适应式图形学习具有语义推广性,用于域调整.

Zefeng Zheng, Shaohua Teng, Luyao Teng

    IEEE transactions on pattern analysis and machine intelligence
    |March 3, 2025
    PubMed
    概括

    本研究介绍了具有语义推广性的自适应图形学习 (AGLSP),以改善域名适应. AGLSP有效地捕获了个性化和本地知识,增强了跨领域的知识传输,以提高模型性能.

    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 域调整 (DA) 旨在弥合标记源域和未标记目标域之间的差距.
    • 现有的基于语义的DA方法经常与个性化的类内和本地类间知识获取作斗争.
    • 这种限制阻碍了跨领域的全面知识转移.

    研究的目的:

    • 提出一种新的域调整方法,即具有语义推广性的自适应图形学习 (AGLSP).
    • 解决在跨领域环境中获得个性化和本地知识的局限性.
    • 增强源域和目标域之间语义知识的转移.

    主要方法:

    • AGLSP使用自适应图形嵌入与语义指导 (AGE-SG) 来估计目标样本的推广性,并学习特定领域的组件.
    • 语义上可推广的样本增强 (SPSE) 提炼了使用多粒度的类内和目标样本挖掘的特征区分能力.
    • 具有隐含语义保存的自适应图形学习 (AGL-ISP) 提取标签细分性的共同点,即使是从不可推广的目标样本.

    主要成果:

    • 拟议的AGLSP方法在域调整任务中表现出卓越的性能.
    • 在七个数据集上进行了广泛的实验,验证了AGLSP方法的有效性.
    • 通过学习更丰富的语义信息,AGLSP成功地转移了更多的跨领域知识.

    更多相关视频

    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
    07:35

    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

    Published on: October 13, 2023

    1.5K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    348

    相关实验视频

    Last Updated: May 24, 2025

    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

    475
    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
    07:35

    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

    Published on: October 13, 2023

    1.5K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    348

    结论:

    • 通过解决现有方法的局限性,AGLSP为域调整提供了一个强大的框架.
    • 多语义细分化和以目标样本为导向的策略显著改善了知识转移.
    • 这种方法显示出各种跨领域学习应用的巨大潜力.