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

452
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...
452
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.8K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
1.8K
Observational Learning01:12

Observational Learning

213
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...
213
Introduction to Learning01:18

Introduction to Learning

478
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...
478
Cognitive Learning01:21

Cognitive Learning

433
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...
433
Purposive Learning01:22

Purposive Learning

146
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
146

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

Training a Dynamic Growing Mixture Model for Lifelong Learning.

IEEE transactions on neural networks and learning systems·2025
Same author

Continual Unsupervised Generative Modeling.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Lifelong Dual Generative Adversarial Nets Learning in Tandem.

IEEE transactions on cybernetics·2023
Same author

Co-attention enabled content-based image retrieval.

Neural networks : the official journal of the International Neural Network Society·2023
Same author

Characterising and dissecting human perception of scene complexity.

Cognition·2022
Same author

Dynamic Self-Supervised Teacher-Student Network Learning.

IEEE transactions on pattern analysis and machine intelligence·2022
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: Jul 24, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K

终身生成对抗自动编码器

Fei Ye, Adrian G Bors

    IEEE transactions on neural networks and learning systems
    |July 6, 2023
    PubMed
    概括
    此摘要是机器生成的。

    终身学习系统现在可以使用新的终身生成对抗自编码器 (LGAA) 来避免灾难性的遗忘. 这种人工智能模型在不失去过去的知识的情况下学习新信息,增强了持续学习能力.

    更多相关视频

    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

    628
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    444

    相关实验视频

    Last Updated: Jul 24, 2025

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
    09:47

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

    Published on: December 15, 2023

    1.1K
    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

    628
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    444

    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 深度学习 (Deep Learning) 是一种深度学习.

    背景情况:

    • 终身学习使得人们能够不断地获取信息,而不会忘记,这对人工智能至关重要.
    • 现代神经网络在顺序学习时面临灾难性的遗忘,失去过去的知识.
    • 生成重复机制 (GRM) 使用生成器 (VAE,GAN) 来缓解遗忘.

    研究的目的:

    • 从理论上分析基于GRM的终身学习系统中的忘记行为.
    • 解决现有的生成重复方法的推断局限性.
    • 提出一种新型模型,即终身生成对抗自编码器 (LGAA),以实现有效的终身学习.

    主要方法:

    • 开发了一个理论框架来表达忘记作为增加模型风险.
    • 提出了终身生成对抗自动编码器 (LGAA),集成了一个生成重复网络和三个推理模型.
    • 实施LGAA来推断不同类型的潜在变量,以增强学习.

    主要成果:

    • LGAA成功地学习了新的视觉概念,而不会忘记以前获得的知识.
    • 提出的理论框架为GRM系统中的忘记动态提供了洞察力.
    • 实验结果验证了LGAA在持续学习场景中的有效性.

    结论:

    • 对于人工智能中的灾难性遗忘,LGAA提供了一个强有力的解决方案.
    • 该模型在广泛的下游任务中展示了适用性.
    • 通过将生成重复与改进的推理能力相结合,LGAA推动了终身学习领域的发展.