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相关概念视频

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
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Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Calibration Curves: Linear Least Squares01:20

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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
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Multi-input and Multi-variable systems01:22

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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贝叶斯对接与深度线性网络的贝叶斯对接

Boris Hanin1, Alexander Zlokapa2,3

  • 1Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08540.

Proceedings of the National Academy of Sciences of the United States of America
|May 30, 2023
PubMed
概括
此摘要是机器生成的。

深度神经网络在无限深度实现最佳预测. 增加深度有助于在广泛的网络中选择模型,有效深度取决于层,数据和宽度.

关键词:
贝叶斯的推理是贝叶斯的推理.深度学习 (Deep Learning) 是一种深度学习.线性网络 线性网络神经网络的神经网络的神经网络

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科学领域:

  • 深度学习理论理论 深度学习理论
  • 计算神经科学是一种计算神经科学.
  • 统计建模 统计建模

背景情况:

  • 了解神经网络架构 (深度,宽度) 和数据集大小对模型性能的相互作用至关重要.
  • 线性网络为深度学习中的理论分析提供了一个可操作的模型.

研究的目的:

  • 为线性网络提供关于深度,宽度和数据集大小的完整理论解决方案.
  • 为了获得预测后部和贝叶斯模型证据的非对称表达式.
  • 阐明网络深度在最佳预测和模型选择中的作用.

主要方法:

  • 贝叶斯推理与高斯重量先验和平均平方误差损失.
  • 使用Meijer-G函数用于非对称表达式.
  • 应用梅耶尔-G函数的新型非对称扩展.

主要成果:

  • 在线网络中的预测后部和贝叶斯模型证据的衍生非对称表达式.
  • 证明了具有数据不可知先验的无限深度线性网络可以产生最佳预测.
  • 展示了贝叶斯模型在广泛网络中的证据在无限深度上得到最大化.

结论:

  • 无限深度为线性网络提供了原则上的优势,特别是在数据不可知先验的情况下.
  • 增加网络深度有利于在宽线性网络中进行模型选择.
  • 引入了一个"有效深度"指标,该指标控制了大数据极限中的后部结构.