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相关概念视频

Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Determination of Expected Frequency01:08

Determination of Expected Frequency

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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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Poisson Probability Distribution01:09

Poisson Probability Distribution

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A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.0K
Sample Size Calculation01:19

Sample Size Calculation

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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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相关实验视频

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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随机修剪:根据预期缩放因子的道稀疏性.

Chuanmeng Sun1,2, Jiaxin Chen1,2, Yong Li3

  • 1North University of China, State Key Laboratory of Dynamic Measurement Technology, Taiyuan, Shanxi, China.

PeerJ. Computer science
|September 14, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了EXP,这是一种用于深度神经网络的新型结构化修剪方法. 基于其预期扩展因子,EXP有效地删除冗余道,显著降低计算成本,同时保持高精度.

关键词:
道剪裁 道剪裁图像的分类图像的分类.模型的压缩压缩.随机稀疏的 随机稀疏的

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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:

  • 深度神经网络 (DNN) 模型的压缩和加速对于高效的部署至关重要.
  • 现有的修剪策略通常涉及复杂的计算和复杂的子网络识别过程.
  • 卷积神经网络 (CNN) 通过结构化修剪提供了优化机会.

研究的目的:

  • 提出一种新的结构化修剪方法,EXP,以实现高效的DNN模型压缩.
  • 为了解决现有的修剪技术在计算开销和复杂性方面的局限性.
  • 为了利用道矩阵元素和预期缩放比率之间的线性关系,实现有效的修剪.

主要方法:

  • 引入了一种新的结构化修剪方法EXP.
  • 在卷积层中识别和随机删除具有类似预期缩放因子 () 的道.
  • 这种方法诱导随机稀疏性,创建非冗余和非唯一的子网络.

主要成果:

  • 在各种网络中,EXP实现了浮点操作 (FLOP) 的显著减少.
  • 在CIFAR-10上,ResNet-56 FLOPs减少了71.9%,只有0.23%的Top-1精度损失.
  • 在ILSVRC-2012上,ResNet-50 FLOPs下降了60.0%,Top-1准确性损失为1.13%.

结论:

  • 拟议的EXP方法提供了一种高效和有效的方法,用于在CNN中进行结构化修剪.
  • EXP成功地降低了模型复杂性和计算要求,而不会大幅降低准确性.
  • 该方法为通过优化子网络生成加速深度学习模型提供了实际解决方案.