Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Survival Tree

Survival Tree

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...

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

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...

Neuroplasticity

Neuroplasticity

Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.

Neural Circuits

Neural Circuits

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...

Neural Regulation

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.

You might also read

Related Articles

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

Sort by

Same author

Lung-Protective Ventilation Strategies for Relief from Ventilator-Associated Lung Injury in Patients Undergoing Craniotomy: A Bicenter Randomized, Parallel, and Controlled Trial.

Oxidative medicine and cellular longevity·2017

Same author

Electrochemical Oxidation of EDTA in Nuclear Wastewater Using Platinum Supported on Activated Carbon Fibers.

International journal of environmental research and public health·2017

Same author

Novel biomimetic enzyme for sensitive detection of superoxide anions.

Talanta·2017

Same author

The Expression of Formyl Peptide Receptor 1 is Correlated with Tumor Invasion of Human Colorectal Cancer.

Scientific reports·2017

Same author

An excited state underlies gene regulation of a transcriptional riboswitch.

Nature chemical biology·2017

Same author

(-)-Epigallocatechin-3-gallate attenuates myocardial injury induced by ischemia/reperfusion in diabetic rats and in H9c2 cells under hyperglycemic conditions.

International journal of molecular medicine·2017

Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

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

Same journal

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

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

Same journal

IGFD-Net: Illumination-guided frequency decoupling for polarization image fusion.

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

Same journal

Multiple-Strategies dung beetle optimizer and its applications in engineering optimization and bankruptcy prediction.

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

Same journal

Aggregating global-scale pixel-wise forgery cues within a graph.

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

Same journal

Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under aperiodic DoS attacks.

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

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 7, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

DMPP: Differentiable multi-pruner and predictor for neural network pruning.

Jiaxin Li¹, Bo Zhao², Derong Liu³

¹School of Automation, Guangdong University of Technology, Guangzhou 510006, China.

Neural Networks : the Official Journal of the International Neural Network Society

|January 8, 2022

Summary

This summary is machine-generated.

This study introduces a differentiable multi-pruner and predictor (DMPP) for automated neural network pruning. DMPP optimizes network structures efficiently, outperforming existing methods on benchmark datasets.

Keywords:

Differentiable structure search Model compression Multi-pruner Neural network pruning Performance predictor

Related Experiment Videos

Last Updated: Oct 7, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Artificial Intelligence
Machine Learning
Computer Vision

Background:

Neural network pruning is crucial for reducing over-parameterized models.
Traditional pruning methods rely on manual expertise and are often time-consuming and suboptimal.
Existing heuristic search methods face challenges in discrete search spaces.

Purpose of the Study:

To develop an automated neural network pruning method.
To overcome the limitations of manual experience and heuristic search in pruning.
To improve the efficiency and performance of pruned neural networks.

Main Methods:

Developed a differentiable multi-pruner and predictor (DMPP) framework.
The pruner uses learnable parameters for continuous representation of pruning ratios.
A neural network-based predictor accelerates structure search via gradient-based optimization.
Incorporated multi-pruner for enhanced search efficiency and knowledge distillation for performance improvement.

Main Results:

DMPP successfully automates neural network pruning.
The method achieved superior performance compared to state-of-the-art techniques.
Experiments were conducted on CIFAR-10, CIFAR-100, and ImageNet datasets using VGGNet and ResNet architectures.

Conclusions:

The proposed DMPP offers an effective and efficient approach for automatic neural network pruning.
Gradient-based optimization combined with prediction accelerates the discovery of optimal network structures.
The method demonstrates significant improvements in pruned network performance and search efficiency.