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Integral expressing the amount of overlap of one function as it is shifted over another
In mathematics (in particular, functional analysis), convolution is a mathematical operation on two functions f and g that produces a third function f∗g
Convolution
Theorem in mathematics
In mathematics, the convolution theorem states that under suitable conditions the Fourier transform of a convolution of two functions (or signals) is the
Convolution_theorem
Type of feedforward neural network
A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep
Convolutional_neural_network
Topics referred to by the same term
mathematics, convolution is a binary operation on functions. Circular convolution Convolution theorem Titchmarsh convolution theorem Dirichlet convolution Infimal
Convolution_(disambiguation)
Mathematical operation on arithmetical functions
In mathematics, Dirichlet convolution (or divisor convolution) is a binary operation defined for arithmetic functions; it is important in number theory
Dirichlet_convolution
Neural network technology
neural networks, a convolutional layer is a type of network layer that applies a convolution operation to the input. Convolutional layers are some of
Convolutional_layer
Mathematical operation
Circular convolution, also known as cyclic convolution, is a special case of periodic convolution, which is the convolution of two periodic functions that
Circular_convolution
Free convolution is the free probability analog of the classical notion of convolution of probability measures. Due to the non-commutative nature of free
Free_convolution
Matrix used in image processing to alter an image
In image processing, a kernel, convolution matrix, or mask is a small matrix used for blurring, sharpening, embossing, edge detection, and more. This
Kernel_(image_processing)
Algorithm to smooth data points
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
Savitzky–Golay_filter
Type of error-correcting code using convolution
represents the 'convolution' of the encoder over the data, which gives rise to the term 'convolutional coding'. The sliding nature of the convolutional codes facilitates
Convolutional_code
Probability distribution of the sum of random variables
The convolution/sum of probability distributions arises in probability theory and statistics as the operation in terms of probability distributions that
Convolution of probability distributions
Convolution_of_probability_distributions
Mathematical concept
space of convolution quotients is a field of fractions of a convolution ring of functions: a convolution quotient is to the operation of convolution as a
Convolution_quotient
Mathematical operation in signal processing
discrete convolution is the discrete analog of the multidimensional convolution of functions on Euclidean space. It is also a special case of convolution on
Multidimensional discrete convolution
Multidimensional_discrete_convolution
Artificial reverberation effect
electromechanical transducers to create vibrations in large plates of sheet metal. Convolution reverb uses impulse responses to record the reverberation of physical
Reverb_effect
Mathematical inequality about the convolution of two functions
In mathematics, Young's convolution inequality is a mathematical inequality about the convolution of two functions, named after William Henry Young. In
Young's convolution inequality
Young's_convolution_inequality
In mathematics, negacyclic convolution is a convolution between two vectors x = ( x 1 , … , x n ) {\displaystyle \mathbf {x} =(x_{1},\dots ,x_{n})} and
Negacyclic_convolution
scale convolution of two functions s ( t ) {\displaystyle s(t)} and r ( t ) {\displaystyle r(t)} , also known as their logarithmic convolution or log-volution
Logarithmic_convolution
Function in discrete mathematics
partial differential equations, and to perform other operations such as convolutions or multiplying large integers. Since the DFT deals with a finite amount
Discrete_Fourier_transform
Method for visualizing vector fields
In scientific visualization, line integral convolution (LIC) is a method to visualize a vector field (such as fluid motion) at high spatial resolutions
Line_integral_convolution
Convolution
category theory, Day convolution is an operation on functors that can be seen as a categorified version of function convolution. It was first introduced
Day_convolution
convolution is a special subset of convolution operations in which the convolution kernel is symmetric across its zero point. Many common convolution-based
Symmetric_convolution
The Titchmarsh convolution theorem describes the properties of the support of the convolution of two functions. It was proven by Edward Charles Titchmarsh
Titchmarsh convolution theorem
Titchmarsh_convolution_theorem
Topics referred to by the same term
products, bounding the product of two quantities Young's convolution inequality, bounding the convolution product of two functions Young's inequality for integral
Young's_inequality
Mathematical concept
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
Convolution_power
Family of computer vision models designed for efficient inference on mobile devices
MobileNet is a family of convolutional neural network (CNN) architectures designed for image classification, object detection, and other computer vision
MobileNet
science, a convolutional deep belief network (CDBN) is a type of deep artificial neural network composed of multiple layers of convolutional restricted
Convolutional deep belief network
Convolutional_deep_belief_network
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
Convolution for optical broad-beam responses in scattering media
Convolution_for_optical_broad-beam_responses_in_scattering_media
Extension of cubic spline interpolation
accomplished using either Lagrange polynomials, cubic splines, or cubic convolution algorithm. In image processing, bicubic interpolation is often chosen
Bicubic_interpolation
Reconstruction of a filtered signal
In mathematics, deconvolution is the inverse of convolution. Both operations are used in signal processing and image processing. For example, it may be
Deconvolution
Deep learning technique
One was the learned absolute positional encoding previously used in convolutional seq2seq. The other was the designed, sinusoidal absolute positional
Positional_encoding
Convolutional neural network structure
LeNet is a series of convolutional neural network architectures created by a research group at AT&T Bell Laboratories between 1988 and 1998, centered
LeNet
Family of convolutional neural networks
Inception is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed
Inception (deep learning architecture)
Inception_(deep_learning_architecture)
Decodes a bitstream with the Viterbi algorithm
that has been encoded using a convolutional code or trellis code. There are other algorithms for decoding a convolutionally encoded stream (for example
Viterbi_decoder
Function which maps a tuple of sequences into a sequence of tuples
In computer science, zipping is a function which maps a tuple of sequences into a sequence of tuples. This name zip derives from the action of a zipper
Zipping_(computer_science)
Discrete Fourier transform for prime sizes
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
Rader's_FFT_algorithm
Method in signal processing
is the traditional name for an efficient way to evaluate the discrete convolution between a very long signal x [ n ] {\displaystyle x[n]} and a finite
Overlap–save_method
Method in signal processing
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
Overlap–add_method
Influential 2012 deep convolutional neural network
AlexNet is a convolutional neural network architecture developed for image classification tasks, notably achieving prominence through its performance
AlexNet
Type of artificial neural network
consists of three sequential convolutional layers and a residual connection. The first layer in this block is a 1×1 convolution for dimension reduction (e
Residual_neural_network
2026 Marvel Studios film
focus on Peter, though he criticized occasional pacing issues and plot convolutions. David Ehrlich at IndieWire gave the film a C−, describing it as a "dull
Spider-Man:_Brand_New_Day
In statistics, the Hájek–Le Cam convolution theorem states that any regular estimator in a parametric model is asymptotically equivalent to a sum of two
Hájek–Le Cam convolution theorem
Hájek–Le_Cam_convolution_theorem
Integral transform and linear operator
The Hilbert transform is given by the Cauchy principal value of the convolution with the function 1 / ( π t ) {\displaystyle 1/(\pi t)} (see § Definition)
Hilbert_transform
Series of convolutional neural networks for image classification
The VGGNets are a series of convolutional neural networks (CNNs) developed by the Visual Geometry Group (VGG) at the University of Oxford. The VGG family
VGGNet
Directional cubic convolution interpolation (DCCI) is an edge-directed image scaling algorithm created by Dengwen Zhou and Xiaoliu Shen. By taking into
Directional cubic convolution interpolation
Directional_cubic_convolution_interpolation
Mathematical model which is both linear and time-invariant
found directly using convolution: y(t) = (x ∗ h)(t) where h(t) is called the system's impulse response and ∗ represents convolution (not to be confused
Linear_time-invariant_system
Gyrus of the primary auditory cortex of the brain
temporal gyrus, also called Heschl's gyrus (/ˈhɛʃəlz ˈdʒaɪrəs/) or Heschl's convolutions, is a gyrus found in the area of each primary auditory cortex buried
Transverse_temporal_gyrus
Device for suppressing part of a signal
the behavior of the filter as a convolution of the time-domain input with the filter's impulse response. The convolution theorem, which holds for Laplace
Filter_(signal_processing)
Class of artificial neural networks
implement different flavors of message passing, started by recursive or convolutional constructive approaches. A 2022 position paper argued that many architectures
Graph_neural_network
Function whose graph is 0, then 1, then 0 again, in an almost-everywhere continuous way
{\sin(\pi af)}{\pi af}}=a\ \operatorname {sinc} _{\pi }{(af)}.} The self convolution of the dis-continuous rectangular function results in the triangular
Rectangular_function
Aspect of probability theory
distribution. Addition of random variables, on the other hand, are the convolution of their probability distributions. Let X and Y be independent random
Sum of normally distributed random variables
Sum_of_normally_distributed_random_variables
Fitting an approximating function to data
a matrix transformation is called convolution. Thus the matrix is also called convolution matrix or a convolution kernel. In the case of simple series
Smoothing
Branch of machine learning
connected networks, deep belief networks, recurrent neural networks, convolutional neural networks, generative adversarial networks, transformers, and
Deep_learning
distribution of the sum of two or more independent random variables is the convolution of their individual distributions. The term is motivated by the fact
List of convolutions of probability distributions
List_of_convolutions_of_probability_distributions
Mathematical transform
of graph structured learning algorithms, such as the widely employed convolutional networks. Given an undirected weighted graph G = ( V , E ) {\displaystyle
Graph_Fourier_transform
Objects that generalize functions
possible to define the convolution of a function with a distribution, or even the convolution of two distributions. Convolution of a test function with
Distribution (mathematical analysis)
Distribution_(mathematical_analysis)
Mathematical theorem on convolved binomial coefficients
In combinatorics, Vandermonde's identity (or Vandermonde's convolution) is the following identity for binomial coefficients: (m+nr)=∑k=0r(mk)(nr−k) for
Vandermonde's_identity
Response of an optical system to a point source of light
everywhere in the imaging space, the image of a complex object is then the convolution of that object and the PSF. The PSF can be derived from diffraction integrals
Point_spread_function
Generalization of the Legendre transformation
functions. The infimal convolution of two functions has a geometric interpretation: The (strict) epigraph of the infimal convolution of two functions is
Convex_conjugate
Deep learning model structure
multilayer perceptron networks, these layers are stacked together. The Convolutional layer is typically used for image analysis tasks. In this layer, the
Layer_(deep_learning)
Type of large language model
reservoir computing Boltzmann machine Restricted GAN Diffusion model SOM Convolutional neural network U-Net LeNet AlexNet DeepDream Neural field Neural radiance
Generative pre-trained transformer
Generative_pre-trained_transformer
Concept in machine learning
parameter space. From 2014 to 2015, tensor methods become more common in convolutional neural networks (CNNs). Tensor methods organize neural network weights
Tensor_(machine_learning)
Mathematical operation
specifically in mathematical analysis, the Cauchy product is the discrete convolution of two infinite series. It is named after the French mathematician Augustin-Louis
Cauchy_product
Machine learning model for vision processing
if they were token embeddings. ViTs were designed as alternatives to convolutional neural networks (CNNs) in computer vision applications. They have different
Vision_transformer
of the symbol so the linear convolution of a frequency-selective multipath channel can be modeled as circular convolution, which in turn may transform
Cyclic_prefix
Mathematical form
the convolution. If ∫−∞∞|f(t)|dt<∞and∫−∞∞|g(t)|dt<∞, then the integral (f∗g)(t):=∫−∞∞f(τ)⋅g(t−τ)dτ is well defined and is called the convolution. Under
Product_(mathematics)
Mathematical algorithm
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
Chirp_Z-transform
Formal power series
integral transformations Knuth's article "Convolution Polynomials" defines a generalized class of convolution polynomial sequences by their special generating
Generating_function
Functions in harmonic analysis mathematics
1)} estimates. A singular integral of convolution type is an operator T {\displaystyle T} defined by convolution with a kernel K {\displaystyle K} that
Singular_integral
Signal (re‑)construction algorithm
expressed as the convolution of an infinite impulse train with a sinc function: x(t)=(∑n=−∞∞T⋅x(nT)⏟x[n]⋅δ(t−nT))∗(1Tsinc(tT)), where the convolution of two functions
Whittaker–Shannon interpolation formula
Whittaker–Shannon_interpolation_formula
Integration kernels for smoothing out sharp features
smooth functions approximating nonsmooth (generalized) functions, via convolution. Intuitively, given a (generalized) function, convolving it with a mollifier
Mollifier
Square matrix of cyclically shifted rows
operations. Algebraically, multiplication by a circulant matrix is circular convolution on the cyclic group Z / n Z {\displaystyle \mathbb {Z} /n\mathbb {Z}
Circulant_matrix
Transformation of a mathematical sequence
under the binomial convolution. There is also another binomial convolution in the mathematical literature. The binomial convolution of arithmetical functions
Binomial_transform
within the mathematical theory of probability, Buzen's algorithm (or convolution algorithm) is an algorithm for calculating the normalization constant
Buzen's_algorithm
Deep learning framework
Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, originally developed at University of California, Berkeley
Caffe_(software)
Computer scientist (born 1986)
With Alex Krizhevsky and Geoffrey Hinton, he co-created AlexNet, a convolutional neural network. One of the most highly cited computer scientists in
Ilya_Sutskever
Probability distribution
(named after Woldemar Voigt) is a probability distribution given by a convolution of a Cauchy–Lorentz distribution and a Gaussian distribution. It is often
Voigt_profile
Fourier analysis technique applied to sequences
} The significance of this result is explained at circular convolution and fast convolution algorithms. S 2 π ( ω ) {\displaystyle S_{2\pi }(\omega )}
Discrete-time Fourier transform
Discrete-time_Fourier_transform
Type of parallel computing architecture of tightly coupled nodes
convolution. Similarly, n-dimensional convolution can be computed by an n-dimensional array of PEs. Many other implementations of the 1D convolutions
Systolic_array
American mathematician (b. 1949)
doctoral dissertation was titled "Inequalities for strongly singular convolution operators". Fefferman achieved a full professorship at the University
Charles_Fefferman
Summability method used in harmonic analysis
δ {\displaystyle B_{R}^{\delta }} may be written as convolution operators, where the convolution kernel is an approximate identity. As such, in these
Bochner–Riesz_mean
Machine learning model family
Region-based convolutional neural networks (R-CNN) are a family of machine learning models for computer vision, and specifically object detection and
Region-based convolutional neural network
Region-based_convolutional_neural_network
Computational model used in machine learning
weight patterns of multiple nodes. Architectural innovations such as convolutional neural networks (CNNs) significantly improved performance in computer
Neural network (machine learning)
Neural_network_(machine_learning)
Family of computer vision models
EfficientNet is a family of convolutional neural networks (CNNs) for computer vision published by researchers at Google AI in 2019. Its key innovation
EfficientNet
Type of convolutional neural network
U-Net is a convolutional neural network that was developed for image segmentation. The network is based on a fully convolutional neural network whose
U-Net
Measure of the shape of a function
a) are zero for all i < n {\displaystyle i<n} . The raw moment of a convolution h ( t ) = ( f ∗ g ) ( t ) = ∫ − ∞ ∞ f ( τ ) g ( t − τ ) d τ {\textstyle
Moment_(mathematics)
Neural network coding model
The convolutional sparse coding paradigm is an extension of the global sparse coding model, in which a redundant dictionary is modeled as a concatenation
Convolutional_sparse_coding
Study of the properties of codes and their fitness
the output of the system convolutional encoder, which is the convolution of the input bit, against the states of the convolution encoder, registers. Fundamentally
Coding_theory
Mathematical transform that expresses a function of time as a function of frequency
Borel measures, with multiplication given by convolution of measures. With the convention above, convolution corresponds to operator multiplication with
Fourier_transform
Rational mathematical function indexed by integer partitions
In mathematics, Weingarten functions are rational functions indexed by partitions of integers that can be used to calculate integrals of products of matrix
Weingarten_function
Problem in computational complexity theory
the set S + S {\displaystyle S+S} of all pairwise sums as a discrete convolution using the fast Fourier transform, and finally comparing this set to S
3SUM
Type of machine learning model
Yanming (2021). "Review of Image Classification Algorithms Based on Convolutional Neural Networks". Remote Sensing. 13 (22): 4712. Bibcode:2021RemS..
Large_language_model
Object detection system
Once (YOLO) is a series of real-time object detection systems based on convolutional neural networks. First introduced by Joseph Redmon et al. in 2015, YOLO
You_Only_Look_Once
Polish mathematician
is based upon an algebra of the convolution of functions with respect to the Fourier transform. From the convolution product he goes on to define what
Jan_Mikusiński
Multilingual neural machine translation service
new languages, with the release of a new implementation that utilizes convolutional neural networks, and also enhanced the speed and quality of Conversation
Google_Translate
Image scaling algorithm
and other types of photos. waifu2x was inspired by Super-Resolution Convolutional Neural Network (SRCNN). It uses Nvidia's CUDA for computing, although
Waifu2x
estimation and convolution), and Filtering (again in two senses: estimation and convolution). Smoothing (estimation) and smoothing (convolution), despite being
Smoothing problem (stochastic processes)
Smoothing_problem_(stochastic_processes)
Generating high-resolution video frames from given low-resolution ones
frame sequence, k {\displaystyle k} — blur kernel, ∗ {\displaystyle *} — convolution operation, ↓ s {\displaystyle \downarrow {_{s}}} — downscaling operation
Video_super-resolution
Mapping involving integration between function spaces
integration kernels are then biperiodic functions; convolution by functions on the circle yields circular convolution. If one uses functions on the cyclic group
Integral_transform
Covariance and correlation
and neurophysiology. The cross-correlation is similar in nature to the convolution of two functions. In an autocorrelation, which is the cross-correlation
Cross-correlation
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