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相关概念视频

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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Probability Distributions01:32

Probability Distributions

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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
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Binomial Probability Distribution01:15

Binomial Probability Distribution

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A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
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Probability Histograms01:17

Probability Histograms

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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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Probability in Statistics01:14

Probability in Statistics

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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
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Poisson's And Laplace's Equation01:25

Poisson's And Laplace's Equation

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The electric potential of the system can be calculated by relating it to the electric charge densities that give rise to the electric potential. The differential form of Gauss's law expresses the electric field's divergence in terms of the electric charge density.
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相关实验视频

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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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图形概率聚合:从伯努利到波桑分布

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    此摘要是机器生成的。

    我们介绍了图形概率聚合 (GP-Pool),这是一种用于深度图形表示学习的新框架. 通过使用概率子图采样,GP-Pool增强了特征学习,在图形分类任务中表现优于现有的方法.

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    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 图形神经网络的神经网络

    背景情况:

    • 在深度图表表示学习中,图表聚合对于增强受体场和降低计算成本至关重要.
    • 现有的方法通常依赖于确定性选择或随机丢弃,这可能会限制灵活性和性能.

    研究的目的:

    • 为改进图形特征学习提出一个简单但有效的图形概率聚合 (GP-Pool) 框架.
    • 开发新的聚合策略,以捕捉突出的子结构和全球拓.

    主要方法:

    • 开发了一个概率子图采样方法,使用变量边界来实现预期分布.
    • 通过可学习的参考集引入了伯努利图集 (BernPool) 用于采样节点和局部结构.
    • 为了可控制的节点数量,衍生了Poisson分布式聚合 (PoissonPool),并提出了一种混合图形聚合 (HGP) 方法,将采样和聚类结合起来.

    主要成果:

    • 由于其非决定性性质,BernPool捕获了突出的图形子结构,并采用了多样化的节点采样.
    • PoissonPool提供了对节点数量的明确控制,使用较少的变化学习变量.
    • 提出的HGP范式有效地结合了子图的紧性和图的粗化,保留了代表性的子结构和全球拓.

    结论:

    • 与各种图形聚合方法相比,GP-Pool框架显示出更高的性能.
    • 在多个公共图形分类数据集上,GP-Pool 取得了最先进的结果.
    • 在深度图形学习中,概率方法为确定性或随机聚合提供了更有效和更灵活的替代方案.