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Related Concept Videos

Convolution Properties I01:20

Convolution Properties I

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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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Convolution Properties II01:17

Convolution Properties II

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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.
The area property asserts that the area under the...
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Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

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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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Extraction: Partition and Distribution Coefficients01:14

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The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
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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:
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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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Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
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An Efficient Sharing Grouped Convolution via Bayesian Learning.

Tinghuan Chen, Bin Duan, Qi Sun

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    |June 10, 2021
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    Summary
    This summary is machine-generated.

    This study introduces a novel sharing grouped convolution structure and Bayesian framework to significantly reduce parameter redundancy in grouped convolutional neural networks. Experiments show substantial parameter reduction and improved prediction accuracy, enhancing model efficiency.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Grouped convolutional neural networks offer advantages in model performance and parameter efficiency over traditional convolutions.
    • Existing grouped convolution models still suffer from significant parameter redundancy, limiting their practical application.
    • Reducing parameter redundancy is crucial for developing more efficient and deployable deep learning models.

    Purpose of the Study:

    • To propose a novel sharing grouped convolution structure to address parameter redundancy in grouped convolutional neural networks.
    • To develop a Bayesian sharing framework for efficiently transferring standard grouped convolutions into the proposed sharing structure.
    • To improve both model parameter efficiency and prediction accuracy in deep learning models.

    Main Methods:

    • Introduced a sharing grouped convolution structure designed to minimize parameter redundancy.
    • Developed a Bayesian sharing framework incorporating intragroup correlation and intergroup importance priors.
    • Utilized a group LASSO-type algorithm to handle Maximum Type II likelihood estimation for correlation and importance.
    • Implemented iterative updates for the prior mean of sharing kernels.

    Main Results:

    • The proposed sharing grouped convolution structure with the Bayesian framework significantly reduced parameters across various grouped convolutional neural networks.
    • Parameter reduction reached up to 64.17% in general.
    • For ResNeXt-50 on the ImageNet dataset, parameters in grouped convolutional layers were reduced by 96.875%.
    • Top-1 and top-5 accuracies improved to 78.86% and 94.54%, respectively, demonstrating enhanced prediction performance.

    Conclusions:

    • The proposed sharing grouped convolution structure and Bayesian sharing framework effectively reduce parameter redundancy while improving prediction accuracy.
    • This approach offers a significant advancement in developing more efficient and performant grouped convolutional neural networks.
    • The method demonstrates broad applicability across different grouped convolutional architectures and datasets.