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相关概念视频

Weighted Mean00:57

Weighted Mean

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
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Design Example: Aggregate Gradation01:24

Design Example: Aggregate Gradation

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The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
The grading, or particle-size distribution, of sand is determined using sieve analysis, with standard sizes ranging from 150 μm to 10 mm (ASTM No. 100 sieve to 3⁄8 in. sieve). Sand is...
149
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

136
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....
136
Reducing Line Loss01:18

Reducing Line Loss

196
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
196
Downsampling01:20

Downsampling

257
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
257
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

128
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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相关实验视频

Updated: Sep 16, 2025

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
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Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

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渐变感知自适应量化:对于全球不均的重量,具有可学习的剪切值的局部均量化.

Kang Zhou1, Yuning Qiu2, Yuhang Li1

  • 1School of Automation, Guangdong University of Technology, Guangzhou 510006, China.

Neural networks : the official journal of the International Neural Network Society
|July 11, 2025
PubMed
概括

这项研究引入了一种用于神经网络压缩的新非均量化方法. 它通过自适应地学习量化级别和剪切值来提高模型性能,减少错误.

关键词:
可学习的剪切值神经网络的神经网络不统一的定量化方式

相关实验视频

Last Updated: Sep 16, 2025

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

2.7K

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 不统一的量子化通过适应重量分布来增强神经网络的压缩.
  • 由于仅依赖重量密度,传统方法的性能下降.

研究的目的:

  • 开发一种先进的非均量子化技术,以改善神经网络压缩.
  • 解决现有的非统一量子化方法的性能限制.

主要方法:

  • 提出了一种新的非统一的量子化方法,其中包括自适应的量子化水平和自动剪切值学习.
  • 一种局部统一的量子化策略可以在密度较高的区域中提炼量子化.
  • 重量梯度被纳入,以实现最佳的量化级别分配.
  • 一种可学习的,基于线性插值的剪切方法可以最大限度地减少异常影响.

主要成果:

  • 拟议的方法显著减少了量子化误差.
  • 在CIFAR10,CIFAR100,Tiny-ImageNet和ImageNet100数据集上进行验证.
  • 与传统方法相比,在量子化后显示出更好的模型性能.

结论:

  • 新的非统一量子化方法有效地减少了量子化错误,并提高了模型性能.
  • 剪切值和量子化水平的自适应学习对于高效的神经网络压缩至关重要.