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相关概念视频

Deconvolution01:20

Deconvolution

197
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...
197
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

298
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...
298
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...
237
Convolution Properties I01:20

Convolution Properties I

185
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:
185
Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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深度卷积表:没有卷积的深度学习.

Shay Dekel, Yosi Keller, Aharon Bar-Hillel

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    概括
    此摘要是机器生成的。

    我们介绍了卷积表 (CTs),这是一个新的深度网络配方,通过用投票表取代点产品神经元来加速CPU推断. CTs提供了优越的容量计算比率和与传统CNN相比的准确性,特别是在低功耗设备中.

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    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:

    • 卷积层是深度学习中的计算瓶,限制了物联网和CPU等资源有限的设备上的应用程序.
    • 现有的深度学习模型通常依赖于点产品神经元,这可能是计算密集的.

    研究的目的:

    • 提出一种使用卷积表 (CTs) 进行加速的基于CPU的推理的新型深度网络配方.
    • 为了证明CTs可以克服传统卷积层在计算复杂性和效率方面的局限性.

    主要方法:

    • 开发了卷积表 (CTs),利用投票表的层次结构而不是点产品神经元.
    • 每个CT都执行一个行动,将本地图像环境编码为二进制索引,用于表查找.
    • 推导出一种基于软放松和梯度的训练方法,用于CT层次结构.

    主要成果:

    • CT计算复杂性与补丁大小无关,并且与通道优雅地缩放,优于标准卷积层.
    • 深度CT网络表现出普遍的近似属性和与CNN相比的准确性.
    • 与高效的CNN相比,CT在低计算方案中提供了优越的错误速度权衡.

    结论:

    • 卷积表为CPU的高效深度学习推断提供了传统卷积层的有希望的替代方案.
    • CT网络实现了具有竞争力的精度,同时显著提高了计算效率,特别是在边缘设备上.
    • 拟议的CT配方能够在具有有限计算资源的环境中实现有效的深度学习.