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

Observational Learning01:12

Observational Learning

843
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...
843
Associative Learning01:27

Associative Learning

1.3K
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...
1.3K
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.3K
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...
1.3K
Introduction to Learning01:18

Introduction to Learning

972
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...
972
Active Filters01:25

Active Filters

1.3K
Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
1.3K
Deconvolution01:20

Deconvolution

548
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
548

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

<i>PPAN</i> modulates mouse male germ cell development via maintaining nucleolar homeostasis.

Genes & diseases·2024
Same author

Multi-View Integrative Approach For Imputing Short-Chain Fatty Acids and Identifying Key factors predicting Blood SCFA.

bioRxiv : the preprint server for biology·2024
Same author

Effect of mixed probiotics on pulmonary flora in patients with mechanical ventilation: an exploratory randomized intervention study.

European journal of medical research·2024
Same author

A staged approach using machine learning and uncertainty quantification to predict the risk of hip fracture.

Bone reports·2024
Same author

Clinical value of peripheral blood miR-21 and miR-486 combined with CT forearly cancer diagnosis in pulmonary nodulessmoking.

Journal of cardiothoracic surgery·2024
Same author

Application Value and Safety Analysis of Warfarin, Rivaroxaban, and Dabigatran Ester in Elderly Patients With Atrial Fibrillation.

Clinical cardiology·2024

相关实验视频

Updated: Jan 18, 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

为开放式主动域调整提供证据深度学习.

Qing Tian, Jiangsen Yu, Yi Zhao

    IEEE transactions on neural networks and learning systems
    |May 29, 2025
    PubMed
    概括

    本研究介绍了开放式主动域适应 (EOSADA) 的证据深度学习,通过管理预测不确定性来改进向新领域的知识转移. 这种新的方法有效地选择了信息样本,提高了模型性能,而没有结构变化.

    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 计算机视觉 计算机视觉

    背景情况:

    • 开放式域调整 (OSDA) 解决了目标域中的新课程的知识转移挑战.
    • 现有的OSDA方法往往忽视了预测的不确定性,并造成了大量的培训成本.
    • 证据深度学习 (EDL) 模型使用迪里克莱特分布来模拟不确定性,超越标准软max输出.

    研究的目的:

    • 提出一个高效的OSDA方法,考虑到预测不确定性,并最大限度地减少注释开销.
    • 介绍开放式主动域适应 (EOSADA) 的证据深度学习.
    • 通过有效地选择信息化目标域样本来提高OSDA中的模型性能.

    主要方法:

    • 实现EDL以创建一个开放集分类器,通过Dirichlet分布替换softmax.
    • 根据目标域数据的不确定性和语义相似性,开发了一种两轮样本选择策略.
    • 专注于平衡已知和新课程的选择,以获取信息样本.

    主要成果:

    • 拟议的EOSADA方法在OSDA场景中表现出卓越的性能.
    • 该策略有效地确定了已知和新型类别的信息样本.
    • 在不改变底层模型架构的情况下实现了显著的性能增长.

    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

    1.0K

    相关实验视频

    Last Updated: Jan 18, 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
    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

    1.0K

    结论:

    • 欧萨达为开放式域调整提供了强大而高效的解决方案.
    • 利用EDL和战略样本选择解决了传统OSDA方法的关键局限性.
    • 这种方法在有限的注释预算下是有效的,最大限度地提高了复杂的域调整任务的性能.