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Updated: Jun 10, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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超参数推与卷积神经网络集成

Liping Deng, Wen-Sheng Chen, Binbin Pan

    IEEE transactions on neural networks and learning systems
    |October 18, 2024
    PubMed
    概括

    本研究介绍了深度学习,特别是卷积神经网络 (CNN),用于超参数推. 这种新的方法有效地捕捉了数据集特征和超参数性能之间的复杂关系,优于现有方法.

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    相关概念视频

    Convolution Properties II01:17

    Convolution Properties II

    The important convolution properties include width, area, differentiation, and integration properties.
    The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
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    Convolution: Math, Graphics, and Discrete Signals01:24

    Convolution: Math, Graphics, and Discrete Signals

    In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
    To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
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    Multi-input and Multi-variable systems01:22

    Multi-input and Multi-variable systems

    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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    Prediction Intervals01:03

    Prediction Intervals

    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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    Deconvolution01:20

    Deconvolution

    Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
    Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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    Convolution Properties I01:20

    Convolution Properties I

    Convolution computations can be simplified by utilizing their inherent properties.
    The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
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