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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

48
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
48
Group Design02:01

Group Design

8.9K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
8.9K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

36
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
36

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

A fluorescence spectrometry method for vitamin B6 determination based on its coordination reaction with Fe<sup>3</sup>.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same author

Targeted metabolomics reveals tricarboxylic acid cycle-related metabolites serve as predictive biomarkers for trastuzumab resistance in human epidermal growth factor receptor 2-positive breast cancer.

Journal of molecular medicine (Berlin, Germany)·2025
Same author

Clinical Deployment of Interpretable AI: Bridging Routine Clinical Tests and Proteomic Signatures for Preeclampsia Risk Stratification.

Current drug targets·2025
Same author

Smug1 alleviates the reproductive toxicity of 5-FU through functioning in rRNA quality control.

Scientific reports·2025
Same author

Analysis of the potential regulatory mechanisms of female and latent genital tuberculosis affecting ovarian reserve function using untargeted metabolomics.

Scientific reports·2024
Same author

Fabrication of gelatin methacryloyl/graphene oxide conductive hydrogel for bone repair.

Journal of biomaterials science. Polymer edition·2023

相关实验视频

Updated: Jun 21, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

685

OTAMatch:使用PseudoNCE进行最佳运输分配,用于半监督学习.

Jinjin Zhang, Junjie Liu, Debang Li

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |July 15, 2024
    PubMed
    概括
    此摘要是机器生成的。

    OTAMatch通过将伪标签重新定义为最佳传输问题来增强半监督学习,减少确认偏差并改善数据利用. 这种新的框架在具有挑战性的基准标准上取得了最先进的结果.

    更多相关视频

    Design and Analysis for Fall Detection System Simplification
    08:05

    Design and Analysis for Fall Detection System Simplification

    Published on: April 6, 2020

    10.7K
    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
    05:30

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

    Published on: September 8, 2023

    531

    相关实验视频

    Last Updated: Jun 21, 2025

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
    03:37

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

    Published on: March 1, 2024

    685
    Design and Analysis for Fall Detection System Simplification
    08:05

    Design and Analysis for Fall Detection System Simplification

    Published on: April 6, 2020

    10.7K
    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
    05:30

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

    Published on: September 8, 2023

    531

    科学领域:

    • 机器学习 机器学习
    • 计算机科学 计算机科学

    背景情况:

    • 半监督学习 (SSL) 经常使用自主训练与一致性规范化.
    • 现有的方法使用值启发式来管理标签噪声,但这可能会丢弃有价值的数据.
    • 确认偏见和不充分利用歧视性信息是关键的挑战.

    研究的目的:

    • 推出OTAMatch,一个新的SSL框架,解决基于值的伪标签的局限性.
    • 通过有效利用高可信度数据来减轻确认偏差.
    • 为了提高SSL算法的稳定性和性能,在杂的环境中.

    主要方法:

    • 重构伪标签作为一个最佳运输 (OT) 赋值问题,通过凸最小化和Sinkhorn-Knopp算法解决.
    • 整合了epsilon-greedy后部规范化和课程偏差校正,以实现强大的OT任务.
    • 引入了PseudoNCE,以最大限度地提高相互信息的交换,并平衡融合速度与性能.

    主要成果:

    • 在各种SSL基准中,OTAMatch表现出了竞争力的表现.
    • 与ImageNet上的SoftMatch相比,实现了显著的9.45%的错误率降低 (100K标签分割).
    • 在具有挑战性的场景中,与最先进的SSL算法相比,其表现明显优于其他算法.

    结论:

    • OTAMatch提供了一种强大而有效的半监督学习方法,特别是在杂的环境中.
    • 最佳的运输配方和综合策略提高了数据利用率和模型性能.
    • 该框架代表了解决确认偏差和提高SSL有效性的重大进展.