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

Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

12.6K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
12.6K
Neural Circuits01:25

Neural Circuits

1.4K
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...
1.4K
Block Diagram Reduction01:22

Block Diagram Reduction

258
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
258
Visual System01:26

Visual System

632
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
632
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

324
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
324
Graphical and Analytic Representation of Sinusoids01:20

Graphical and Analytic Representation of Sinusoids

442
Analyzing two sinusoidal voltages with equal amplitude and period but different phases on an oscilloscope, an instrument used to display and analyze waveforms, involves a three-step process.
The first step is measuring the peak-to-peak value, which is twice the amplitude of the sinusoid. This provides information about the maximum voltage swing of the waveform.
Secondly, the period and angular frequency are determined. The period is the time taken for one complete cycle of the waveform, while...
442

You might also read

Related Articles

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

Sort by
Same author

Explanation strategies in humans versus current explainable artificial intelligence: Insights from image classification.

British journal of psychology (London, England : 1953)·2024
Same author

UHRF1 inhibition epigenetically reprograms cancer stem cells to suppress the tumorigenic phenotype of hepatocellular carcinoma.

Cell death & disease·2023
Same author

Hydrodynamic and anthropogenic disturbances co-shape microbiota rhythmicity and community assembly within intertidal groundwater-surface water continuum.

Water research·2023
Same author

Raman spectromics method for fast and label-free genotype screening.

Biomedical optics express·2023
Same author

Integrative multi-omics deciphers the spatial characteristics of host-gut microbiota interactions in Crohn's disease.

Cell reports. Medicine·2023
Same author

Ginsenoside Rg3 Protects Mouse Islet β-Cells Injured by High Glucose.

Indian journal of microbiology·2023

Related Experiment Video

Updated: Aug 4, 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

592

Towards Efficient Visual Simplification of Computational Graphs in Deep Neural Networks.

Rusheng Pan, Zhiyong Wang, Yating Wei

    IEEE Transactions on Visualization and Computer Graphics
    |April 4, 2023
    PubMed
    Summary

    This study introduces visual simplification techniques to improve the visualization of complex deep neural network (DNN) computational graphs. The new methods enhance the recognition and diagnosis of DNN models by reducing graph complexity.

    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

    9.3K
    Visualization of Cortical Modules in Flattened Mammalian Cortices
    08:49

    Visualization of Cortical Modules in Flattened Mammalian Cortices

    Published on: January 22, 2018

    13.0K

    Related Experiment Videos

    Last Updated: Aug 4, 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

    592
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.3K
    Visualization of Cortical Modules in Flattened Mammalian Cortices
    08:49

    Visualization of Cortical Modules in Flattened Mammalian Cortices

    Published on: January 22, 2018

    13.0K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Deep neural networks (DNNs) utilize computational graphs, represented as data flow diagrams (DFDs), comprising tensors and operators.
    • Existing visualization tools struggle with the complexity and scale of large DNNs, such as BERT.

    Purpose of the Study:

    • To develop advanced visual simplification techniques for effectively visualizing large-scale DNN computational graphs.
    • To enhance the usability and performance of DNN model recognition and diagnosis.

    Main Methods:

    • Proposed techniques include cycle removal, module-based edge pruning, and isomorphic subgraph stacking.
    • Developed an interactive visualization system capable of handling DNN computational graphs with up to 10,000 elements.
    • Integrated these methods into the open-source DNN visualization toolkit, MindInsight.

    Main Results:

    • The visualization tool reduces the number of displayed elements by an average of 60%.
    • Demonstrated improved performance in recognizing and diagnosing DNN models through practical usage scenarios.
    • The system effectively visualizes complex computational graphs that were previously unmanageable.

    Conclusions:

    • The proposed visual simplification techniques significantly improve the visualization of complex DNN computational graphs.
    • The interactive system enhances the efficiency of DNN model analysis and debugging.
    • The integration into MindInsight provides a valuable open-source resource for the deep learning community.