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

2.0K
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...
2.0K
Neural Regulation01:37

Neural Regulation

40.9K
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.
40.9K
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
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

223
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 of...
223
Gradient and Del Operator01:14

Gradient and Del Operator

3.7K
In mathematics and physics, the gradient and del operator are fundamental concepts used to describe the behavior of functions and fields in space. The gradient is a mathematical operator that gives both the magnitude and direction of the maximum spatial rate of change. Consider a person standing on a mountain. The slope of the mountain at any given point is not defined unless it is quantified in a particular direction. For this reason, a "directional derivative" is defined, which is a vector...
3.7K

You might also read

Related Articles

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

Sort by
Same author

Depth-Induced Saliency Comparison Network for the Diagnosis of Alzheimer's Disease via Joint Analysis of Stimuli and Eye Movements.

IEEE journal of biomedical and health informatics·2026
Same author

CovSegNet: A Multi Encoder-Decoder Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans.

IEEE transactions on artificial intelligence·2023
Same author

BAWGNet: Boundary aware wavelet guided network for the nuclei segmentation in histopathology images.

Computers in biology and medicine·2023
Same author

Automatic Metric Search for Few-Shot Learning.

IEEE transactions on neural networks and learning systems·2023
Same author

Privacy Enhancing Machine Learning via Removal of Unwanted Dependencies.

IEEE transactions on neural networks and learning systems·2021
Same author

Cross-Scale Residual Network: A General Framework for Image Super-Resolution, Denoising, and Deblocking.

IEEE transactions on cybernetics·2021

Related Experiment Video

Updated: Nov 5, 2025

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.6K

Exploiting Operation Importance for Differentiable Neural Architecture Search.

Yuan Zhou, Xukai Xie, Sun-Yuan Kung

    IEEE Transactions on Neural Networks and Learning Systems
    |May 17, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces EoiNAS, a novel neural architecture search (NAS) method. EoiNAS uses an operation importance indicator and Markov chains to efficiently discover high-performance models for image classification and semantic segmentation.

    Related Experiment Videos

    Last Updated: Nov 5, 2025

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

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.6K

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Vision

    Background:

    • Differentiable Neural Architecture Search (NAS) methods reduce computational costs.
    • Current NAS methods rely on architecture weights, which may not accurately reflect operation importance.
    • This limitation can lead to suboptimal architecture selection.

    Purpose of the Study:

    • To propose a novel indicator for accurately representing operation importance in NAS.
    • To develop an effective NAS scheme, termed EoiNAS, guided by this indicator.
    • To enhance search efficiency and accuracy using a high-order Markov chain-based strategy.

    Main Methods:

    • Introduced a new indicator to quantify the importance of operations within neural network architectures.
    • Developed the EoiNAS framework, leveraging the proposed indicator for guided architecture search.
    • Implemented a high-order Markov chain strategy to effectively prune the search space.

    Main Results:

    • EoiNAS successfully identified high-performance architectures for image classification and semantic segmentation.
    • The proposed indicator proved effective in guiding the NAS process.
    • The Markov chain strategy contributed to improved search efficiency and accuracy.

    Conclusions:

    • The EoiNAS method offers an effective approach to neural architecture search by accurately assessing operation importance.
    • This method enhances the efficiency and accuracy of discovering optimal neural network architectures.
    • EoiNAS demonstrates strong performance across diverse computer vision tasks.