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相关概念视频

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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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Introduction to Test of Independence01:21

Introduction to Test of Independence

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In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
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Truncation in Survival Analysis01:09

Truncation in Survival Analysis

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Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
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Column Efficiency: Rate Theory01:12

Column Efficiency: Rate Theory

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The rate theory of chromatography provides quantitative insight into the shapes and widths of elution bands. These bands are based on the random-walk mechanism governing molecular migration within a column. The Gaussian profile of chromatographic bands arises from the cumulative effect of random molecular motions as they progress through the column.
During elution, a solute molecule experiences numerous transitions between stationary and mobile phases, exhibiting irregular residence times in...
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Phylogenetic Trees03:21

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Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.
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Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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根据信息容量和独立性进行过器修剪.

Xiaolong Tang, Shuo Ye, Yufeng Shi

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    此摘要是机器生成的。

    本研究引入了一种用于卷积神经网络 (CNN) 的新过器修剪方法. 它有效地识别和删除不重要的过器,显著减少模型大小和计算,以最小的准确性损失.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 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:

    • 卷积神经网络 (CNN) 广泛使用,但计算密集.
    • 现有的过器修剪方法面临偏差选择和高计算成本的挑战.
    • 有效的CNN压缩对于实际部署至关重要.

    研究的目的:

    • 为CNN推出一种新的,可解释的,轻量级的过器修剪方法.
    • 解决现有的过器修剪技术在偏差和计算开销方面的局限性.
    • 为了提高CNN的效率,而不会显著降低性能.

    主要方法:

    • 开发了一种多视角过器评估方法.
    • 引入了"信息容量",使用可解释的来衡量个别波器的重要性.
    • 设计了"信息独立性"来评估过器之间的相关性.
    • 实现了以特征为导向的近似方法,以实现高效的度量计算.

    主要成果:

    • 在基准数据集上大大降低了计算成本和模型大小.
    • 与最先进的方法相比,证明了卓越的性能.
    • 对于ILSVRC-2012上的ResNet-50,FLOP降低了77.4%,参数降低了69.3%,只有2.64%的精度下降.

    结论:

    • 拟议的过器修剪方法是有效的,高效的和实用的.
    • 它为CNN压缩提供了一种轻量级和可解释的方法.
    • 该方法显著提高了CNN的性能,同时保持了高精度.