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

Survival Tree01:19

Survival Tree

109
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
109
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
Law of Independent Assortment02:03

Law of Independent Assortment

55.9K
While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
55.9K
Overview Of Cell Separation And Isolation01:20

Overview Of Cell Separation And Isolation

5.7K
Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.
5.7K
Law of Segregation01:49

Law of Segregation

66.2K
When crossing pea plants, Mendel noticed that one of the parental traits would sometimes disappear in the first generation of offspring, called the F1 generation, and could reappear in the next generation (F2). He concluded that one of the traits must be dominant over the other, thereby causing masking of one trait in the F1 generation. When he crossed the F1 plants, he found that 75% of the offspring in the F2 generation had the dominant phenotype, while 25% had the recessive phenotype.
66.2K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

2.5K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
2.5K

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

The 2020 US Decennial Census is more private than you (might) think.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training.

Proceedings of the National Academy of Sciences of the United States of America·2021
查看所有相关文章

相关实验视频

Updated: Jul 17, 2025

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

深度学习中的数据分离定律.

Hangfeng He1,2, Weijie J Su3

  • 1Department of Computer Science, University of Rochester, Rochester, NY 14627.

Proceedings of the National Academy of Sciences of the United States of America
|August 28, 2023
PubMed
概括

研究人员发现了一种定量定律,解释了深度神经网络 (DNN) 如何在中间层按类别分离数据. 这一发现为DNN提供了洞察力,有助于AI设计和解释.

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 计算机科学 计算机科学

背景情况:

  • 深度学习模型已经推进了人工智能,但它们的"黑子"性质使设计和解释复杂化.
  • 了解中间层数据处理对于人工智能开发和高风险应用至关重要.

研究的目的:

  • 研究深度神经网络 (DNN) 如何在它们的中间层中处理数据.
  • 揭示一个基本的定量法规,规范DNN中的数据分离.

主要方法:

  • 分析各种深度神经网络架构的中间层中的数据分离.
  • 在不同数据集的培训过程中观察法律的出现.

主要成果:

  • 确定了一个简单的定量定律,它规范了DNN如何在所有层中按类别分离数据.
  • 每个层都以恒定的几何速率持续改进数据分离.
  • 在培训期间,在多个网络架构和数据集中观察到这一规律.

结论:

  • 发现法提供了对DNN数据处理的基本理解.
  • 它为设计更好的AI架构提供了实用指南.
关键词:
不变的几何速率是恒定的.数据分离数据的分离.深度学习是一种深度学习.中间层是中间层.

更多相关视频

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

568
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K

相关实验视频

Last Updated: Jul 17, 2025

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
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

568
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K
  • 这些发现可以提高模型的稳定性,样本外性能和预测可解释性.