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

Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

375
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
375
Improving Translational Accuracy02:07

Improving Translational Accuracy

8.5K
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...
8.5K
Mean Absolute Deviation01:13

Mean Absolute Deviation

2.5K
The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
2.5K
Multiple Comparison Tests01:13

Multiple Comparison Tests

3.8K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
3.8K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.4K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.4K
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

157
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
157

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

GSDeformer: Direct, Real-Time and Extensible Cage-Based Deformation for 3D Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same author

Computation-aided design of rod-shaped nanoparticles for tumoral targeting.

Journal of controlled release : official journal of the Controlled Release Society·2025
Same author

An end-to-end implicit neural representation architecture for medical volume data.

PloS one·2025
Same author

Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets.

Nature methods·2024
Same author

Exploring therapeutic targets for molecular therapy of idiopathic pulmonary fibrosis.

Science progress·2024
Same author

Computation-aided Design of Rod-Shaped Janus Base Nanopieces for Improved Tissue Penetration and Therapeutics Delivery.

bioRxiv : the preprint server for biology·2024

相关实验视频

Updated: May 21, 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

448

对于深度网络进行基准测试的实用概括度量.

Mengqing Huang1, Hongchuan Yu2, Jianjun Zhang1

  • 1National Centre for Computer Animation, Bournemouth University, Poole, BH12 5BB, UK.

Scientific reports
|March 22, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种实用度量来评估深度学习模型的概括性,发现它取决于准确性和数据多样性. 大多数现有的理论估计与实际测量有很差的相关性.

更多相关视频

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

966

相关实验视频

Last Updated: May 21, 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

448
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

966

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 在深度学习模型中估计概括错误对于实际应用和理论验证都至关重要.
  • 目前的研究缺乏标准化的方法来对深度网络泛化进行基准测试,并验证理论预测.
  • 实际评估对于弥合理论估计与现实绩效之间的差距至关重要.

研究的目的:

  • 引入一种实用的概括度量,用于对各种深度学习网络进行基准测试.
  • 提出一种用于验证理论概括估计的新型试验台.
  • 量化模型准确性,数据多样性和概括能力之间的关系.

主要方法:

  • 开发一种新的实践概括度量.
  • 为深度学习模型创建一个比较测试平台.
  • 对拟议的指标与现有的理论概括估计进行比较分析.

主要成果:

  • 在分类中的深度网络泛化受到分类准确性和未见数据多样性的影响.
  • 拟议的指标量化了模型准确性和数据多样性,提供了直观的权衡评估.
  • 大多数现有的理论概括估计表明与新测试台的实际测量有很差的相关性.

结论:

  • 拟议的指标提供了对深度学习模型概括的定量评估.
  • 当前的理论概括估计与实际表现之间存在重大差异.
  • 这项工作突出了现有理论的局限性,并激励进一步研究更准确的概括评估方法.