Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Pie Chart01:04

Pie Chart

13.0K
A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each indicating numerical proportions. It is also used to show the relative sizes of data in a single chart.
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...
13.0K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Relative molecule self-attention transformer.

Journal of cheminformatics·2024
Same author

A Comparative Study of Deterministic and Stochastic Models of Microstructure Evolution during Multi-Step Hot Deformation of Steels.

Materials (Basel, Switzerland)·2023
Same author

Generative Imputation and Stochastic Prediction.

IEEE transactions on pattern analysis and machine intelligence·2020
Same author

TAPER: Time-Aware Patient EHR Representation.

IEEE journal of biomedical and health informatics·2020
Same author

Dynamic Feature Acquisition Using Denoising Autoencoders.

IEEE transactions on neural networks and learning systems·2018
Same author

The nationwide program of allergic disease prevention as an implementation of GARD guidelines in Poland.

Journal of thoracic disease·2018
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
查看所有相关文章

相关实验视频

Updated: May 6, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.9K

一个大规模合成图形数据集生成框架.

Sajad Darabi, Piotr Bigaj, Dawid Majchrowski

    IEEE transactions on neural networks and learning systems
    |March 27, 2025
    PubMed
    概括
    此摘要是机器生成的。

    研究人员开发了一种合成图形生成工具,用于创建用于深度图形学习的大规模数据集. 这个工具有助于开发和对比用于现实世界的应用程序的图形算法.

    更多相关视频

    Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development
    13:01

    Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

    Published on: April 10, 2016

    35.5K
    Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools
    07:58

    Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

    Published on: November 11, 2020

    5.9K

    相关实验视频

    Last Updated: May 6, 2026

    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
    12:08

    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

    Published on: August 13, 2014

    24.9K
    Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development
    13:01

    Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

    Published on: April 10, 2016

    35.5K
    Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools
    07:58

    Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

    Published on: November 11, 2020

    5.9K

    科学领域:

    • 计算机科学 计算机科学
    • 机器学习 机器学习
    • 数据科学数据科学数据科学

    背景情况:

    • 深度图形学习对于欺诈检测和推系统等任务越来越重要.
    • 公共图形数据集的有限可用性和大小阻碍了生产规模应用的开发.

    研究的目的:

    • 为了解决大规模图形数据集的稀缺问题.
    • 引入一种新的合成图表生成工具,用于可扩展的数据创建.

    主要方法:

    • 提出了一个框架,用于合成图形生成的参数模型.
    • 模型可以随机初始化或适应现有的专有数据集.
    • 允许创建具有万亿边缘和数十亿节点的图形.

    主要成果:

    • 在各种数据集中展示了可通用性,保留了结构和特征分布.
    • 成功地将合成数据集扩展到各种尺寸以进行基准测试.
    • 促进了原型开发和探索新型图形学习应用程序.

    结论:

    • 合成图表生成工具有效地克服了当前数据集的局限性.
    • 该框架支持深度图形学习中的可扩展模型开发和基准测试.
    • 在GitHub上发布代码,以促进研究和应用程序开发.