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...

You might also read

Related Articles

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

Sort by

Same author

Intelligent Temperature and Pressure Sensing Decoupling Systems in Multimodal Nanonetwork-based Electronic Textiles.

ACS nano·2026

Same author

Dispersal and water age of the plumes from the four outlets of the Pearl River during the dry winter.

Marine pollution bulletin·2026

Same author

Humidity-Triggered Spontaneous Phase Separation Enables Multifunctional 3D Porous Composites for Piezoresistive Mechanosensory and Electromagnetic Interference Shielding.

Small (Weinheim an der Bergstrasse, Germany)·2026

Same author

Efficient and Scalable Point Cloud Generation With Sparse Point-Voxel Diffusion Models.

IEEE transactions on neural networks and learning systems·2025

Same author

Non-Uniform Entropy-Constrained <i>L</i><sub>∞</sub> Quantization for Sparse and Irregular Sources.

Entropy (Basel, Switzerland)·2025

Same author

A Functionally Conserved yet Dynamically Evolving Toolkit Underpinning Molluscan Biomineralization: Insights From Shell and Radula.

Integrative zoology·2025

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: Nov 18, 2025

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

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

Published on: December 15, 2023

MaskLayer: Enabling scalable deep learning solutions by training embedded feature sets.

Remco Royen¹, Leon Denis¹, Quentin Bolsee¹

¹Vrije Universiteit Brussel, Faculty of Engineering, Department of Electronics and Informatics, Brussels, Belgium.

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

|February 7, 2021

Summary

This summary is machine-generated.

This study introduces MaskLayer, a novel neural network layer enabling quality scalability in deep learning models. This generic solution integrates seamlessly, offering embedded feature sets for enhanced performance in various applications.

Keywords:

Compression Deep learning Point clouds Quality scalable Scalability Semantic hashing

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Related Experiment Videos

Last Updated: Nov 18, 2025

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

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

Published on: December 15, 2023

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Deep learning excels in many domains but lacks inherent quality scalability.
Existing methods struggle to adapt output quality dynamically.

Purpose of the Study:

Introduce a generic neural network layer for quality scalability.
Enhance deep learning models with adaptable output quality by design.
Demonstrate the layer's effectiveness in diverse applications.

Main Methods:

Developed MaskLayer, a novel neural network layer for feedforward networks.
Implemented embedded feature sets through structured feature vectors during training.
Proposed a masked optimizer and balancing gradient rescaling for performance improvement.

Main Results:

MaskLayer integration incurs minimal performance cost.
Achieved excellent results when applied to point cloud compression and semantic hashing.
Demonstrated the layer's generality and applicability in existing non-scalable networks.

Conclusions:

MaskLayer provides the first generic deep learning solution for quality scalability.
The proposed methods offer a practical approach to enhance deep learning model adaptability.
This work opens new avenues for quality-aware deep learning applications.