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Related Concept Videos

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.
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The important convolution properties include width, area, differentiation, and integration properties.
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Convolution computations can be simplified by utilizing their inherent properties.
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Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
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Group polarization is the strengthening of an original group attitude following the discussion of views within a group (Teger & Pruitt, 1967). That is, if a group initially favors a viewpoint, after discussion the group consensus is likely a stronger endorsement of the viewpoint. Conversely, if the group was initially opposed to a viewpoint, group discussion would likely lead to stronger opposition.
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Leveling is a surveying procedure used to determine elevation differences between distant points. Elevation refers to the vertical distance above or below a reference datum, typically mean sea level (MSL). In the United States, elevations are often referenced to the mean sea level station at Father Point Rimouski along the St. Lawrence Seaway. To make the datum accessible, permanent markers are established throughout the region. These markers, called benchmarks, have known elevations. If the...
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Transferring Cognitive Tasks Between Brain Imaging Modalities: Implications for Task Design and Results Interpretation in fMRI Studies
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Two-level group convolution.

Youngkyu Lee1, Jongho Park2, Chang-Ock Lee1

  • 1Department of Mathematical Sciences, KAIST, Daejeon 34141, Republic of Korea.

Neural Networks : the Official Journal of the International Neural Network Society
|August 5, 2022
PubMed
Summary
This summary is machine-generated.

We introduce a novel two-level group convolution method to enhance convolutional neural network performance. This approach improves robustness with more groups and supports multi-GPU parallel computation.

Keywords:
Block Jacobi approximationGroup convolutionParallel computationTwo-level method

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Group convolution reduces computation time in convolutional neural networks but suffers performance degradation with many groups.
  • Standard convolution is computationally intensive, dominating neural network training time.

Purpose of the Study:

  • To propose a new convolution methodology, "two-level" group convolution, that maintains performance with an increased number of groups.
  • To develop a method suitable for multi-GPU parallel computation and robust against performance degradation.

Main Methods:

  • Interpreting group convolution as a one-level block Jacobi approximation from numerical analysis.
  • Introducing a coarse-level structure to promote intergroup communication without creating bottlenecks.
  • Ensuring additional work from the coarse-level structure is efficiently processed in distributed memory systems.

Main Results:

  • Demonstrated robustness of the two-level group convolution method with respect to the number of groups.
  • Verified efficient processing of the coarse-level structure in distributed memory systems.
  • Numerical results confirm the proposed method's superiority over existing group convolution approaches.

Conclusions:

  • The proposed two-level group convolution effectively addresses the performance degradation issue associated with increasing group numbers.
  • This method offers significant improvements in execution time, memory efficiency, and overall performance for deep learning models.
  • The approach is well-suited for multi-GPU parallel computation, enhancing scalability.