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

Neural Circuits01:25

Neural Circuits

1.8K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.8K
Survival Tree01:19

Survival Tree

178
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
178
Introduction to Learning01:18

Introduction to Learning

591
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...
591
Reducing Line Loss01:18

Reducing Line Loss

215
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
215

You might also read

Related Articles

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

Sort by
Same author

DermSynth3D: Synthesis of in-the-wild annotated dermatology images.

Medical image analysis·2024
Same author

Home-Based Rehabilitation System for Stroke Survivors: A Clinical Evaluation.

Journal of medical systems·2020
Same author

Localized Trajectories for 2D and 3D Action Recognition.

Sensors (Basel, Switzerland)·2019
Same author

Home self-training: Visual feedback for assisting physical activity for stroke survivors.

Computer methods and programs in biomedicine·2019
Same author

Prototype-Incorporated Emotional Neural Network.

IEEE transactions on neural networks and learning systems·2017
Same author

Deformation Based Curved Shape Representation.

IEEE transactions on pattern analysis and machine intelligence·2017
Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

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

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

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

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

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

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

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

Related Experiment Video

Updated: Oct 7, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

674

Why Is Everyone Training Very Deep Neural Network With Skip Connections?

Oyebade K Oyedotun, Kassem Al Ismaeil, Djamila Aouada

    IEEE Transactions on Neural Networks and Learning Systems
    |January 5, 2022
    PubMed
    Summary
    This summary is machine-generated.

    Deep neural networks (DNNs) with skip connections are easier to train and generalize better than plain networks. Skip connections prevent information loss in DNNs, unlike plain networks which suffer from singularity problems as depth increases.

    More Related Videos

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.4K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    547

    Related Experiment Videos

    Last Updated: Oct 7, 2025

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    674
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.4K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    547

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Deep neural networks (DNNs) often utilize skip connections to address optimization challenges and enhance generalization.
    • Plain networks (PlainNets), lacking skip connections, become untrainable beyond a certain depth, hindering performance.
    • The precise mechanisms by which skip connections benefit DNNs remain incompletely understood.

    Purpose of the Study:

    • To theoretically analyze the role of skip connections in training very deep neural networks.
    • To compare the optimization and generalization capabilities of DNNs with and without skip connections.
    • To elucidate the mathematical underpinnings of skip connection efficacy using linear algebra and random matrix theory.

    Main Methods:

    • Theoretical analysis of deep neural networks using concepts from linear algebra and random matrix theory.
    • Comparative study of PlainNets against popular skip connection architectures like Residual Networks (ResNets) and ResNeXt.
    • Investigation of information flow and representation properties within network layers.

    Main Results:

    • PlainNets exhibit progressive information loss in hidden representations due to singularity problems as network depth increases, leading to optimization difficulties.
    • DNNs with skip connections circumvent singularity issues, preserving information and enabling effective optimization and improved generalization with greater depth.
    • Skip connections facilitate the retention of full information, crucial for training deeper and more performant models.

    Conclusions:

    • Skip connections are critical for overcoming optimization hurdles and enhancing generalization in deep neural networks.
    • The theoretical framework confirms that skip connections maintain representational fidelity, enabling the training of significantly deeper models.
    • Architectures like ResNets and ResNeXt demonstrate the practical benefits of skip connections in deep learning.