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

Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

575
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...
575
Associative Learning01:27

Associative Learning

408
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...
408
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

109
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...
109
Probability Laws01:49

Probability Laws

40.9K
Overview
40.9K
Purposive Learning01:22

Purposive Learning

122
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...
122
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

833
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
833

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

AdaptCMVC++: Robust and Flexible Adaptation to Incremental Views in Continual Multi-view Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

ProPy : Building interactive and efficient prompt pyramids upon CLIP for partially relevant video retrieval.

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

A robust and versatile deep learning model for prediction of the arterial input function in dynamic small animal [<sup>18</sup>F] FDG PET imaging.

EJNMMI research·2026
Same author

Machine learning-based lineage prediction from antimicrobial susceptibility testing phenotypes for <i>Escherichia coli</i> sequence type 131 clade C surveillance across infection types.

Microbial genomics·2026
Same author

<i>Enterococcus lactis</i> is ecologically and genetically distinct from the major opportunistic pathogen <i>Enterococcus faecium</i>.

Microbial genomics·2025
Same author

The Conditional Cauchy-Schwarz Divergence With Applications to Time-Series Data and Sequential Decision Making.

IEEE transactions on pattern analysis and machine intelligence·2025

相关实验视频

Updated: Jul 11, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K

在生成模型中,通过条件先验进行歧视性的多式模式学习.

Rogelio A Mancisidor1, Michael Kampffmeyer2, Kjersti Aas3

  • 1Department of Data Science and Analytics, BI Norwegian Business School, Nydalsveien 37, 0484 Oslo, Norway.

Neural networks : the official journal of the International Neural Network Society
|November 6, 2023
PubMed
概括

本研究引入了一种新的条件多式模式模型,以改进深度生成模型. 它增强了缺少数据的联合表示,在分类和生成任务中取得了最先进的结果.

关键词:
生成型模型是一种生成型模型.多模式学习是多模式学习.代表性的学习学习.变量自动编码器变量自动编码器

更多相关视频

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.6K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K

相关实验视频

Last Updated: Jul 11, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.6K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K

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:

  • 具有潜变量的深度生成模型从多模数据中学习.
  • 现有的方法可能会在表示学习和生成过程之间发生冲突,未能嵌入模式信息.
  • 一个常见的挑战是下游任务中缺少的模式或标签,尽管有完整的培训数据.

研究的目的:

  • 解决在联合代表和缺失的模式之间嵌入相互信息的变化下限的局限性.
  • 引入一种新的有条件的多模式歧视模型,以改善代表性学习.
  • 为了最大限度地提高联合代表团和缺失的模式之间的相互信息.

主要方法:

  • 开发了一种新的条件多模式歧视模型.
  • 使用了有信息的先前分布.
  • 优化了一个无概率的目标函数,以最大限度地提高相互信息.

主要成果:

  • 通过广泛的实验,证明了拟议模型的显著好处.
  • 在下游分类中取得了最先进的结果.
  • 在声学倒置,图像生成和注释生成任务中展示了卓越的性能.

结论:

  • 拟议的模型有效地抵消了变化的下界限制相互信息的问题.
  • 该模型成功地最大限度地提高了联合代表和缺失的模式之间的相互信息.
  • 这种方法为缺少数据的多模式学习提供了强大的解决方案.