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相关概念视频

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures 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. Among the various sampling methods used by...
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Random Variables01:09

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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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在边缘计算中,用于深度神经网络的随机素描学习.

Bin Li1,2, Peijun Chen3, Hongfu Liu3

  • 1School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China. Binli@bupt.edu.cn.

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概括

随机草图学习 (Rosler) 通过在训练过程中压缩模型来实现高效的微小人工智能. 这种方法显著减少了设备上人工智能应用程序的内存,计算和能源使用.

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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
  • 计算机工程 计算机工程

背景情况:

  • 深度神经网络 (DNN) 具有巨大的潜力,但需要大量的计算资源和内存,阻碍了低成本边缘设备的部署.
  • 现有的轻量级DNN方法在弥合实际微小的人工智能实施的资源差距方面面临挑战.
  • 在资源有限的硬件上需要高效的AI解决方案,这对于更广泛的科学和工业采用至关重要.

研究的目的:

  • 引入一种新的架构,即随机草图学习 (Rosler),用于计算高效的微小人工智能.
  • 开发一个通用框架,用于在训练时压缩模型,使紧的AI模型能够直接学习.
  • 为了促进AI应用程序的计算效率高的设备内学习.

主要方法:

  • 开发了一种压缩在训练过程中的框架,名为随机素描学习 (Rosler).
  • 实施了一个通用框架,直接学习紧模型,优化资源效率.
  • 在各种模型和数据集上验证了方法,以评估性能和效率的提高.

主要成果:

  • 与完全连接的DNN相比,通过16位量子化实现了大约50-90×的显著内存减少.
  • 在低成本硬件上显示了超过180×的显著加速度计算.
  • 在部署的边缘设备上减少了大约10×的能源消耗.

结论:

  • 随机草图学习 (Rosler) 为在边缘设备上部署高效的微小人工智能提供了一个可行的解决方案.
  • 开发的框架能够实现显著的内存,计算和能源节约,克服了以前的限制.
  • 这种方法为人工智能在各种资源有限的科学和工业应用中广泛采用铺平了道路.