Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
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
Same author

Deep-learning-derived input function in dynamic [<sup>18</sup>F]FDG PET imaging of mice.

Frontiers in nuclear medicine·2024
Same journal

A practical design of backdoor trigger under frequency-based orthogonality constraints.

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

EEG fine-grained visual semantic decoding via a multimodal framework.

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

Collaborative-adversarial jailbreaking: A propagation-aware attack framework for multi-agent code generation systems.

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

Theoretical analysis of the denoising autoencoder using Tweedie's formula.

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

Frequency-based cross-attention fusion network for RGB-D salient object detection.

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

HTNet: A self-supervised heterogeneous triple network for multi-modal data.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

Related Experiment Video

Updated: Jul 11, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K

Discriminative multimodal learning via conditional priors in generative models.

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
Summary
This summary is machine-generated.

This study introduces a new conditional multi-modal model to improve deep generative models. It enhances joint representations for missing data, achieving state-of-the-art results in classification and generation tasks.

Keywords:
Generative modelsMultimodal learningRepresentation learningVariational autoencoder

More Related Videos

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

Related Experiment Videos

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

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Vision

Background:

  • Deep generative models with latent variables learn from multi-modal data.
  • Existing methods may conflict between representation learning and generative processes, failing to embed modality information.
  • A common challenge is missing modalities or labels during downstream tasks, despite full training data.

Purpose of the Study:

  • To address limitations of variational lower bounds in embedding mutual information between joint representations and missing modalities.
  • To introduce a novel conditional multi-modal discriminative model for improved representation learning.
  • To maximize mutual information between joint representations and missing modalities.

Main Methods:

  • Developed a novel conditional multi-modal discriminative model.
  • Utilized an informative prior distribution.
  • Optimized a likelihood-free objective function to maximize mutual information.

Main Results:

  • Demonstrated significant benefits of the proposed model through extensive experimentation.
  • Achieved state-of-the-art results in downstream classification.
  • Showcased superior performance in acoustic inversion, image generation, and annotation generation tasks.

Conclusions:

  • The proposed model effectively counteracts issues where variational lower bounds limit mutual information.
  • The model successfully maximizes mutual information between joint representations and missing modalities.
  • This approach offers a robust solution for multi-modal learning with missing data.