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

Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

You might also read

Related Articles

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

Sort by
Same author

Novelty exploration-activated ensemble in the lateral hypothalamus confers analgesic and anxiolytic effects.

Nature communications·2026
Same author

An attention-demanding hunting paradigm engages the superior colliculus-zona incerta circuit mediating analgesia in male mice.

Nature communications·2026
Same author

Nigra-Subthalamic Dopaminergic Circuitry Modulates and Represents Distinct Pain Modality in Physiological and Pain States in Mice.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

SEEK: A simple defense to model hijacking attack.

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

Postgraduate nursing students' experiences in a project-based learning model: a qualitative focus group study.

BMC medical education·2026
Same author

Targeting complement C3 with Tanshinone I decreases microglia-mediated synaptic engulfment to exert antidepressant effects.

Theranostics·2025
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: Jun 29, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.9K

Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects.

Na Li, Chunyi Zhou, Yansong Gao

    IEEE Transactions on Neural Networks and Learning Systems
    |March 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Machine unlearning (MU) enables data deletion from machine learning models without full retraining, addressing the right to be forgotten. This survey maps MU algorithms, verification methods, and applications, focusing on large language models.

    More Related Videos

    Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
    09:11

    Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

    Published on: January 27, 2023

    2.0K
    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
    08:20

    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

    Published on: October 27, 2023

    1.3K

    Related Experiment Videos

    Last Updated: Jun 29, 2026

    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
    09:34

    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

    Published on: September 25, 2021

    3.9K
    Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
    09:11

    Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

    Published on: January 27, 2023

    2.0K
    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
    08:20

    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

    Published on: October 27, 2023

    1.3K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Data Privacy

    Background:

    • Personal digital data is a critical asset, necessitating robust privacy protections.
    • The "right to be forgotten" (RTBF) empowers individuals to request data deletion.
    • Existing machine learning (ML) practices struggle to efficiently remove data's influence post-request.

    Purpose of the Study:

    • To provide a comprehensive survey of the rapidly evolving field of machine unlearning (MU).
    • To categorize MU algorithms, discuss approximate unlearning, and detail verification/evaluation metrics.
    • To explore MU's application in large language models (LLMs) and identify future research directions.

    Main Methods:

    • Extensive literature review and synthesis of existing research on machine unlearning.
    • Development of a fine-grained taxonomy for unlearning algorithms in centralized and distributed settings.
    • Analysis of approximate unlearning, verification techniques, and application-specific challenges.

    Main Results:

    • A structured overview of machine unlearning techniques, including their strengths and weaknesses.
    • Identification of key challenges and proposed solutions for implementing MU, particularly in LLMs.
    • Discussion of potential attacks targeting unlearning processes and methods for their mitigation.

    Conclusions:

    • Machine unlearning is crucial for upholding data privacy rights in the age of AI.
    • Further research is needed to refine MU methods, enhance verification, and secure unlearning processes.
    • This survey serves as a foundational resource for researchers and practitioners in machine unlearning.