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

246
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...
246
Introduction to Learning01:18

Introduction to Learning

480
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...
480
Reversible and Irreversible Processes01:14

Reversible and Irreversible Processes

4.3K
The thermodynamic processes can be classified into reversible and irreversible processes. The processes that can be restored to their initial state are called reversible processes. It is only possible if the process is in quasi-static equilibrium, i.e., it takes place in infinitesimally small steps, and the system remains at equilibrium However, these are ideal processes and do not occur naturally. An ideal system undergoing a reversible process is always in thermodynamic equilibrium within...
4.3K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

134
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
134
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

668
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...
668
Purposive Learning01:22

Purposive Learning

169
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...
169

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

Mixture-of-experts graph transformers for interpretable particle collision detection.

Scientific reports·2025
Same author

NACHOS: Neural Architecture Search for Hardware-Constrained Early-Exit Neural Networks.

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

Micrographia in Parkinson's Disease: Automatic Recognition through Artificial Intelligence.

Movement disorders clinical practice·2025
Same author

Adaptive token selection for scalable point cloud transformers.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Position: Topological Deep Learning is the New Frontier for Relational Learning.

Proceedings of machine learning research·2025
Same author

Spatio-temporal transformers for decoding neural movement control.

Journal of neural engineering·2025
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

IGFD-Net: Illumination-guided frequency decoupling for polarization image fusion.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Multiple-Strategies dung beetle optimizer and its applications in engineering optimization and bankruptcy prediction.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Aggregating global-scale pixel-wise forgery cues within a graph.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under aperiodic DoS attacks.

Neural networks : the official journal of the International Neural Network Society·2026
查看所有相关文章

相关实验视频

Updated: Jul 29, 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

635

使用可逆生成模型进行持续学习.

Jary Pomponi1, Simone Scardapane1, Aurelio Uncini1

  • 1Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Italy.

Neural networks : the official journal of the International Neural Network Society
|May 27, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新方法,通过结合规范化和生成排练来防止神经网络中的灾难性遗忘. 该方法使用规范化流程来有效地维护过去的知识,优于现有技术.

关键词:
灾难性的遗忘.持续的学习 持续的学习机器学习 机器学习规范化流量的流动.

更多相关视频

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K
Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.3K

相关实验视频

Last Updated: Jul 29, 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

635
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K
Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.3K

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:

  • 灾难性遗忘 (CF) 是持续学习的一个主要挑战,在持续学习中,神经网络在接受新任务训练时会覆盖先前获得的知识.
  • 减轻CF现有的方法包括体重调节和排练策略,使用生成模型创建用于排练的合成数据.

研究的目的:

  • 提出一种新的方法,将规范化和基于生成的排练集成在一起,以对抗神经网络中的灾难性遗忘.
  • 在整个培训过程中开发一种有效的记忆方法,在整个培训过程中保持持续的记忆开销.

主要方法:

  • 一个基于规范化流 (NF) 的生成模型,一个概率和可逆的神经网络,在神经网络的内部嵌入上进行训练.
  • 通过利用NF的可逆性,实现了对网络嵌入有关过去任务的简单规范化技术.
  • 提出的方法结合了规范化和基于生成的排练策略的优势.

主要成果:

  • 与文献中最先进的方法相比,提出的方法表现良好.
  • 通过在整个训练过程中使用单个NF,将内存开销保持在恒定的水平.
  • 该方法通过有限的计算能力和内存要求来实现这一目标.

结论:

  • 这种新的方法有效地解决了灾难性遗忘,通过协同结合规范化和生成排练.
  • 使用规范化流程为持续学习提供了高效和可扩展的解决方案.
  • 这种技术为开发更强大,更持久的神经网络模型提供了有前途的方向.