In mathematics, the convolution power is the n-fold iteration of the convolution with itself. Thus if x {\displaystyle x} is a function on Euclidean space...

In mathematics (in particular, functional analysis), convolution is a mathematical operation on two functions f {\displaystyle f} and g {\displaystyle...

A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep...

limit. (The operation of repeatedly taking a convolution of something with itself is called the convolution power.) Pascal's triangle has many properties and...

In mathematics, Dirichlet convolution (or divisor convolution) is a binary operation defined for arithmetic functions; it is important in number theory...

additive and reproductive convolution as well as under scale transformation. Consequently, these models all express a power-law relationship between the...

distorting the signal tendency. This is achieved, in a process known as convolution, by fitting successive sub-sets of adjacent data points with a low-degree...

{x}}_{T}(f)|^{2}\,df,} where the integrand defines the power spectral density: The convolution theorem then allows regarding | x ^ T ( f ) | 2 {\displaystyle...

partial differential equations, and to perform other operations such as convolutions or multiplying large integers. Since it deals with a finite amount of...

rate is 128.6 kbit/s, while its most robust is 21.4 kbit/s. It uses a convolutional code for error detection and correction. The upper layer is usually...

product of the two sequences of coefficients, and is a sort of discrete convolution. With these operations, R N {\displaystyle R^{\mathbb {N} }} becomes...

{\displaystyle u_{1}^{*n}} denotes the n {\displaystyle n} -fold convolution power. Moreover, explicit asymptotes for w n {\displaystyle w_{n}} are known...

a cyclic convolution (the other algorithm for FFTs of prime sizes, Bluestein's algorithm, also works by rewriting the DFT as a convolution). Since Rader's...

In mathematics, deconvolution is the inverse of convolution. Both operations are used in signal processing and image processing. For example, it may be...

LeNet is a series of convolutional neural network architectures created by a research group in AT&T Bell Laboratories during the 1988 to 1998 period,...

section of the beam. However, convolution can be used in certain cases to improve computational efficiency. In order for convolution to be used to calculate...

A.; Allauzen, A. (2023), "Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration", Proceedings of the 40th International Conference...

The depthwise separable convolution decomposes a single standard convolution into two convolutions: a depthwise convolution that filters each input channel...

\mathrm {d} \tau } is well defined and is called the convolution. Under the Fourier transform, convolution becomes point-wise function multiplication. The...

+f_{m}^{*}.} The infimal convolution of two functions has a geometric interpretation: The (strict) epigraph of the infimal convolution of two functions is...

the overlap–add method is an efficient way to evaluate the discrete convolution of a very long signal x [ n ] {\displaystyle x[n]} with a finite impulse...

specifically in mathematical analysis, the Cauchy product is the discrete convolution of two infinite series. It is named after the French mathematician Augustin-Louis...

that has been encoded using a convolutional code or trellis code. There are other algorithms for decoding a convolutionally encoded stream (for example...

Inception is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed...

signals combined by convolution (such as a source and filter) into sums of their cepstra, for linear separation. In particular, the power cepstrum is often...

the diagonal of a matrix Kernel density estimation, the width of the convolution kernel used in statistics Graph bandwidth, in graph theory Coherence...

function F with a power series expansion such that F(0) = 1. We say that a family of polynomials, f0, f1, f2, ..., forms a convolution family if deg fn...

obtain the convolution of a and b, according to the usual convolution theorem. Let us also be more precise about what type of convolution is required...

integration kernels are then biperiodic functions; convolution by functions on the circle yields circular convolution. If one uses functions on the cyclic group...

The sparse nature of velvet noise allows for efficient time-domain convolution, making velvet noise particularly useful for applications where computational...