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

Associative Learning01:27

Associative Learning

1.3K
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...
1.3K
Introduction to Learning01:18

Introduction to Learning

972
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...
972
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.3K
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...
1.3K
Cognitive Learning01:21

Cognitive Learning

1.0K
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...
1.0K
Observational Learning01:12

Observational Learning

843
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...
843

You might also read

Related Articles

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

Sort by
Same author

Transcriptomic analysis reveals gender differences in gene expression profiling of the hypothalamus of rhesus macaque with aging.

Aging·2020
Same author

Metabolomics and correlation network analysis of follicular fluid reveals associations between l-tryptophan, l-tyrosine and polycystic ovary syndrome.

Biomedical chromatography : BMC·2020
Same author

Cumulative live birth rate of low prognosis patients with POSEIDON stratification: a single-centre data analysis.

Reproductive biomedicine online·2020
Same author

Unravelling the effect of sulfur vacancies on the electronic structure of the MoS<sub>2</sub> crystal.

Physical chemistry chemical physics : PCCP·2020
Same author

Family companion between patients with coronavirus disease 2019: a retrospective observational study.

Chinese medical journal·2020
Same author

A method for the quantitative detection of Cas12a ribonucleoproteins.

Chemical communications (Cambridge, England)·2020
Same journal

Recent Advances in Predictive Modeling with Electronic Health Records.

IJCAI : proceedings of the conference·2025
Same journal

ReBandit: Random Effects Based Online RL Algorithm for Reducing Cannabis Use.

IJCAI : proceedings of the conference·2024
Same journal

Predictive Modeling with Temporal Graphical Representation on Electronic Health Records.

IJCAI : proceedings of the conference·2024
Same journal

Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning.

IJCAI : proceedings of the conference·2023
Same journal

Stabilizing and Enhancing Link Prediction through Deepened Graph Auto-Encoders.

IJCAI : proceedings of the conference·2022
Same journal

RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection.

IJCAI : proceedings of the conference·2022
See all related articles

Related Experiment Video

Updated: Jan 19, 2026

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

1.0K

Learning Disentangled Semantic Representation for Domain Adaptation.

Ruichu Cai1, Zijian Li1, Pengfei Wei2

  • 1School of Computers, Guangdong University of Technology, China.

IJCAI : Proceedings of the Conference
|September 27, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel domain adaptation method that disentangles semantic and domain information. This approach achieves state-of-the-art performance by extracting domain-invariant semantic representations.

Related Experiment Videos

Last Updated: Jan 19, 2026

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

1.0K

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Vision

Background:

  • Domain adaptation is crucial for applying models to new data distributions.
  • Existing methods often fail to separate domain-specific from semantic information.
  • Entangled features hinder effective cross-domain generalization.

Purpose of the Study:

  • To develop a method for extracting domain-invariant semantic information.
  • To address the challenge of entangled features in domain adaptation.
  • To improve model performance on unseen domains.

Main Methods:

  • Proposed a novel approach using latent disentangled semantic representation (DSR).
  • Employed a variational auto-encoder to reconstruct latent variables.
  • Utilized a dual adversarial network to disentangle semantic and domain variables.
  • Adapted disentangled semantic latent variables across domains.

Main Results:

  • Achieved state-of-the-art performance on benchmark domain adaptation datasets.
  • Demonstrated effective extraction of domain-invariant semantic information.
  • Successfully disentangled semantic and domain-specific features.

Conclusions:

  • The proposed DSR method offers a significant advancement in domain adaptation.
  • Disentangling latent variables is key to robust cross-domain generalization.
  • The model shows strong potential for real-world applications requiring domain adaptation.