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相关概念视频

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

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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...
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Survival Tree01:19

Survival Tree

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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...
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Residual Plots01:07

Residual Plots

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A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
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Bootstrapping01:24

Bootstrapping

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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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Neural Control of Respiration01:18

Neural Control of Respiration

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The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...
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Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

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Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
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相关实验视频

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Deep Neural Networks for Image-Based Dietary Assessment
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剩余块的强有力的初始化,以便在没有批量规范化的情况下进行有效的reset培训.

Enrico Civitelli, Alessio Sortino, Matteo Lapucci

    IEEE transactions on neural networks and learning systems
    |October 27, 2023
    PubMed
    概括

    体重初始化对于训练无正常化神经网络至关重要. 对ResNet块总和的轻微修改可以实现有效的初始化,在图像数据集上实现具有竞争力的结果,而无需额外的规范化.

    科学领域:

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

    背景情况:

    • 批量规范化是现代神经网络的标准组件.
    • 批量规范化的实际问题刺激了对无规范化架构的研究.
    • 对没有标准化的网络进行有效的培训仍然是一个挑战.

    研究的目的:

    • 调查体重初始化在训练中扮演的角色ResNet类型的无规范化网络.
    • 提出一个简单的修改,以改善这些网络的初始化.
    • 为了证明拟议方法在基准数据集上的有效性.

    主要方法:

    • 引入了一个新的,轻微修改了ResNet块内的总和操作.
    • 这种修改有助于对整个网络进行正确的重量初始化.
    • 修改后的架构在CIFAR-10,CIFAR-100和ImageNet数据集上进行了训练和评估.

    主要成果:

    • 拟议的权重初始化策略使得没有正常化的ResNet类网络的成功训练成为可能.
    • 修改后的架构在CIFAR-10,CIFAR-100和Image.Net上实现了竞争性性能.
    • 不需要额外的规范化或算法更改.

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    结论:

    • 重量初始化对于没有正常化的深度学习模型来说是一个关键因素.
    • 一个简单的修改块总和可以有效地初始化类似ResNet的架构.
    • 这种方法为批量规范化提供了可行的替代方案,而不会影响性能.