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
Attribution Theory00:56

Attribution Theory

13.7K
Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).
13.7K
Fundamental Attribution Error01:14

Fundamental Attribution Error

13.7K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
13.7K
Information Processing Approach01:30

Information Processing Approach

508
The information-processing theory of cognitive development centers on fundamental mental processes, including attention, memory, and problem-solving skills. Researchers in this field examine how cognitive abilities, such as working memory, evolve and influence children's overall development. Studies indicate that children with stronger working memory tend to excel in reading comprehension, math, and problem-solving compared to peers with less efficient memory skills. Low working memory is...
508
Introduction to Learning01:18

Introduction to Learning

923
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...
923
Observational Learning01:12

Observational Learning

817
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...
817

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

Dual modal pathomics model for colorectal cancer early recurrence prediction and mutation landscape analysis.

iScience·2026
Same author

WSO-YOLO11: a wavelet-strip-oriented YOLO11 framework for hot-rolled steel strip surface defect detection.

Scientific reports·2026
Same author

The YAP-TEAD4 inhibitor M511-0965 suppresses triple-negative breast cancer progression by downregulating TGFB2.

Translational oncology·2026
Same author

Piezo1/2 in fibrosis: Cell-type-specific roles, organ-specific pathogenesis, and therapeutic implications.

Burns : journal of the International Society for Burn Injuries·2026
Same author

ERF Transcription Factor NaERF<sub>IDOG</sub> Regulates JA-Induced Defence and Growth Inhibition in Nicotiana attenuata Upon Alternaria alternata Infection.

Plant biotechnology journal·2026
Same author

Genome-Wide Analysis of <i>CsCAX</i> Genes and Functional Characterization of <i>CsCAX3</i> Revealing Its Negative Role in Citrus Bacterial Disease Resistance.

International journal of molecular sciences·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
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

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

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

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

A Survey on Human-Centric Voice-Face Multimodal Learning.

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

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

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

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

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

相关实验视频

Updated: Jan 13, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K

可靠:一个可解释的属性导向的表示学习框架.

Zihan Fang, Shide Du, Ying Zou

    IEEE transactions on neural networks and learning systems
    |October 28, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究引入了一个可靠的表示学习框架,通过将数据解成四个关键属性:忠实性,拓学,不变性和可区分性. 这种方法增强了模型的可靠性,特别是在复杂的多源异质环境中.

    相关实验视频

    Last Updated: Jan 13, 2026

    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    2.3K

    科学领域:

    • 机器学习 机器学习
    • 数据科学数据科学数据科学
    • 人工智能的人工智能

    背景情况:

    • 代表性学习揭示了数据模式,但由于数据质量和异质场景而存在不确定性,影响了可靠性.
    • 现有的方法往往难以将固有的数据知识整合到学习过程中,从而限制了可靠的建模.

    研究的目的:

    • 开发一个可靠的表示学习框架,将数据属性与建模策略连接起来.
    • 提高代表性学习的可信性和可解释性,特别是在复杂的环境中.

    主要方法:

    • 引入了一个可解释的以属性为导向的表示学习框架.
    • 将数据解成四个主要属性:忠实性,拓性,不变性和可区分性.
    • 将这些属性纳入优化衍生框架,并包含可追溯解释性的一般损失条款.

    主要成果:

    • 从框架中衍生出的网络表现出有效性和可靠性,特别是在多源异质场景中.
    • 该框架成功地将深度表示与可靠建模的先前知识集成在一起.
    • 在复杂的环境中取得了有希望的结果,验证了该方法的稳定性.

    结论:

    • 拟议的框架通过解决数据不确定性和增强可解释性,为表示学习提供了可靠的基础.
    • 这种以属性为导向的方法可以扩展到多源异质场景,提供适应性和可靠性.
    • 这项工作为更可靠的AI模型铺平了道路,通过无地将先前的知识整合到深度表示学习中.