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

Long-term Potentiation01:35

Long-term Potentiation

55.1K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
55.1K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

89
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
89
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
Law of Effect01:06

Law of Effect

1.4K
B.F. Skinner, a prominent figure in behavioral psychology, introduced operant conditioning by emphasizing the role of consequences in shaping behavior. This theory builds upon the law of effect proposed by Edward Thorndike, which posits that behaviors followed by satisfying outcomes are likely to be repeated. In contrast, those followed by unsatisfying outcomes are less likely to recur.
Edward Thorndike's foundational work involved studying learning in animals, particularly using puzzle...
1.4K

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

Compressed multi-scale entropy and its application in mechanical fault diagnosis.

The Review of scientific instruments·2026
Same author

Multiomic characterization of malignant pulmonary nodules and development of a methylation-based diagnostic Model.

Journal of translational medicine·2026
Same author

Record-High Resolution X-Ray Imaging With Multi-Component and Near-Infrared Organic Scintillators Enabled by TADF Sensitization.

Angewandte Chemie (International ed. in English)·2026
Same author

Integrative multi-omics profiling reveals distinct evolutionary and immunogenic features of brain oligometastasis in lung adenocarcinoma.

Genome medicine·2026
Same author

Study on electromagnetic characteristics of cylindrical hole defect in variable parameter traction motor shaft based on eddy current effect.

PloS one·2026
Same author

Early diagnosis of extranodal NK/T lymphoma presenting with oral ulcer and lip swelling by metagenomics next-generation sequencing: a case report.

BMC oral health·2026

相关实验视频

Updated: Jun 22, 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

515

通过线性预测提高深度神经网络训练效率和性能.

Hejie Ying1,2, Mengmeng Song3,4, Yaohong Tang1,2

  • 1Ningde Normal University, No. 1 College Road, Ningde, 352101, FuJian, China.

Scientific reports
|July 2, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一个参数线性预测方法,以增强深度神经网络训练. 与标准培训技术相比,新方法提高了准确性并减少了错误.

关键词:
在 DNN 培训中进行 DNN 培训.线性预测 线性预测参数改变法律的参数参数预测 参数预测

更多相关视频

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.9K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

相关实验视频

Last Updated: Jun 22, 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

515
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.9K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

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:

  • 深度神经网络 (DNN) 取得了巨大的成功,但面临着培训挑战.
  • 在DNN训练期间的参数动态提供了优化潜力.
  • 现有的训练方法可能无法充分利用参数行为.

研究的目的:

  • 提出一种用于优化DNN培训效率和绩效的新方法.
  • 为了提高培训效率,利用参数预测.
  • 通过从预测错误中注入噪音来提高DNN性能.

主要方法:

  • 为DNN开发了一个参数线性预测 (PLP) 方法.
  • 在训练期间观察和使用的参数 (权重和偏差) 改变规则.
  • 通过预测错误进行内置的噪音注入,以提高性能.

主要成果:

  • 该PLP方法实现了大约1%的准确度比随机梯度下降 (SGD) 提高.
  • 顶级-1/顶级-5错误减少了大约0.01.
  • 在各种超参数设置中表现出稳定的性能.

结论:

  • 拟议的参数线性预测方法有效地提高了DNN训练效率和性能.
  • PLP为SGD等传统方法提供了可行的替代方案.
  • 该方法显示出稳定性和一致的结果,验证了其实际实用性.