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

Metacognition01:26

Metacognition

372
Metacognition is a conscious process where individuals are aware of their cognitive and executive processes, such as planning before solving a problem or self-monitoring during reading. For instance, a writer may need help with composing a piece. The situation involves a writer who is working on a piece of writing, but while doing so, they realize that something is missing. They notice that their characters lack depth or details. This realization occurs because the writer is reflecting on their...
372
Improving Translational Accuracy02:07

Improving Translational Accuracy

12.1K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
12.1K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.2K
3.2K
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.0K
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...
1.0K
Observational Learning01:12

Observational Learning

520
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...
520
Stereotype Content Model02:16

Stereotype Content Model

15.0K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
15.0K

You might also read

Related Articles

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

Sort by
Same author

ReCL: A Plug-and-Play Module for Enhancing Generalized Category Discovery Using Transport-Based Method to Uncover the Relationship in Samples.

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

LibFewShot: A Comprehensive Library for Few-Shot Learning.

IEEE transactions on pattern analysis and machine intelligence·2023
Same author

Modeling Rationality: Toward Better Performance Against Unknown Agents in Sequential Games.

IEEE transactions on cybernetics·2023
Same author

LncRNA BIRF Promotes Brain Ischemic Tolerance Induced By Cerebral Ischemic Preconditioning Through Upregulating GLT-1 via Sponging miR-330-5p.

Molecular neurobiology·2022
Same author

Toward layered MoS<sub>2</sub> anode for harvesting superior lithium storage.

RSC advances·2022
Same author

Longer-term rates of survival and reintervention after thoracic endovascular aortic repair (TEVAR) for blunt aortic injury: a retrospective population-based cohort study from Ontario, Canada.

Trauma surgery & acute care open·2022
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
Same journal

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

IEEE transactions on neural networks and learning systems·2026
Same journal

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

IEEE transactions on neural networks and learning systems·2026
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
See all related articles

Related Experiment Video

Updated: Nov 2, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

811

Consistent Meta-Regularization for Better Meta-Knowledge in Few-Shot Learning.

Pinzhuo Tian, Wenbin Li, Yang Gao

    IEEE Transactions on Neural Networks and Learning Systems
    |June 9, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces consistent meta-regularization (Con-MetaReg) to improve few-shot learning by reducing data distribution discrepancies. The novel approach enhances meta-knowledge consistency, boosting model performance across various tasks.

    More Related Videos

    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

    809
    Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
    12:55

    Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

    Published on: September 27, 2020

    8.7K

    Related Experiment Videos

    Last Updated: Nov 2, 2025

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
    05:47

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

    Published on: June 13, 2025

    811
    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

    809
    Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
    12:55

    Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

    Published on: September 27, 2020

    8.7K

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Computer Science

    Background:

    • Meta-learning is a key paradigm for few-shot learning.
    • Existing methods often overlook data inconsistency between training and testing sets in few-shot tasks.
    • Effective meta-knowledge should mitigate these data discrepancies.

    Purpose of the Study:

    • To address the data inconsistency issue in few-shot learning.
    • To propose a novel meta-regularization method that leverages prior understanding of meta-knowledge.
    • To enhance the ability of meta-knowledge to maintain consistency between training and test data distributions.

    Main Methods:

    • Introduced a new consistent meta-regularization (Con-MetaReg) technique.
    • Focused on data inconsistency from a distribution perspective.
    • Designed Con-MetaReg to help meta-learning models reduce distribution discrepancies.

    Main Results:

    • Con-MetaReg enhances the consistency of meta-knowledge.
    • The method demonstrably improves the performance of meta-learning models.
    • Effectiveness shown across few-shot regression, classification, and fine-grained classification tasks.

    Conclusions:

    • Consistent meta-regularization is an effective approach for few-shot learning.
    • Addressing data distribution discrepancies is crucial for improving meta-learning.
    • The proposed method offers a significant advancement in few-shot learning performance.