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Photon transport theories inPhysics,Medicine, andStatistics(such as theMonte Carlo method), are commonly used to modellight propagation in tissue. The responses to apencil beamincident on a scattering medium are referred to asGreen's functionsorimpulse responses. Photon transport methods can be directly used to compu... | https://en.wikipedia.org/wiki/Convolution_for_optical_broad-beam_responses_in_scattering_media |
Inmathematics, theconvolution poweris then-fold iteration of theconvolutionwith itself. Thus ifx{\displaystyle x}is afunctiononEuclidean spaceRdandn{\displaystyle n}is anatural number, then the convolution power is defined by
where∗denotes the convolution operation of functions onRdand δ0is theDirac delta distribution... | https://en.wikipedia.org/wiki/Convolution_power |
Inmathematics, a space ofconvolution quotientsis afield of fractionsof a convolutionringof functions: a convolution quotient is to theoperationofconvolutionas aquotientofintegersis tomultiplication. The construction of convolution quotients allows easy algebraic representation of theDirac delta function,integral opera... | https://en.wikipedia.org/wiki/Convolution_quotient |
Inmathematics,deconvolutionis theinverseofconvolution. Both operations are used insignal processingandimage processing. For example, it may be possible to recover the original signal after a filter (convolution) by using a deconvolution method with a certain degree of accuracy.[1]Due to the measurement error of the rec... | https://en.wikipedia.org/wiki/Deconvolution |
Inmathematics,Dirichlet convolution(ordivisor convolution) is abinary operationdefined forarithmetic functions; it is important innumber theory. It was developed byPeter Gustav Lejeune Dirichlet.
Iff,g:N→C{\displaystyle f,g:\mathbb {N} \to \mathbb {C} }are twoarithmetic functions, their Dirichlet convolutionf∗g{\displ... | https://en.wikipedia.org/wiki/Dirichlet_convolution |
Withinsignal processing, in many cases only oneimagewithnoiseis available, and averaging is then realized in a local neighborhood. Results are acceptable if the noise is smaller in size than the smallest objects of interest in the image, but blurring of edges is a serious disadvantage. In the case of smoothing within a... | https://en.wikipedia.org/wiki/Generalized_signal_averaging |
Inprobability theory, theprobability distributionof the sum of two or moreindependentrandom variablesis theconvolutionof their individual distributions. The term is motivated by the fact that theprobability mass functionorprobability density functionof a sum of independent random variables is theconvolutionof their cor... | https://en.wikipedia.org/wiki/List_of_convolutions_of_probability_distributions |
Insystem analysis, among other fields of study, alinear time-invariant(LTI)systemis asystemthat produces an output signal from any input signal subject to the constraints oflinearityandtime-invariance; these terms are briefly defined in the overview below. These properties apply (exactly or approximately) to many impor... | https://en.wikipedia.org/wiki/LTI_system_theory#Impulse_response_and_convolution |
In signal processing,multidimensional discrete convolutionrefers to the mathematical operation between two functionsfandgon ann-dimensional lattice that produces a third function, also ofn-dimensions. Multidimensional discrete convolution is the discrete analog of themultidimensional convolutionof functions onEuclidean... | https://en.wikipedia.org/wiki/Multidimensional_discrete_convolution |
TheTitchmarsh convolution theoremdescribes the properties of thesupportof theconvolutionof two functions. It was proven byEdward Charles Titchmarshin 1926.[1]
Ifφ(t){\textstyle \varphi (t)\,}andψ(t){\textstyle \psi (t)}are integrable functions, such that
almost everywherein the interval0<x<κ{\displaystyle 0<x<\kappa ... | https://en.wikipedia.org/wiki/Titchmarsh_convolution_theorem |
Inlinear algebra, aToeplitz matrixordiagonal-constant matrix, named afterOtto Toeplitz, is amatrixin which each descending diagonal from left to right is constant. For instance, the following matrix is a Toeplitz matrix:
Anyn×n{\displaystyle n\times n}matrixA{\displaystyle A}of the form
is aToeplitz matrix. If thei,j... | https://en.wikipedia.org/wiki/Toeplitz_matrix |
Thereceptive field, orsensory space, is a delimited medium where somephysiological stimulican evoke asensoryneuronal response in specificorganisms.[1]
Complexity of the receptive field ranges from the unidimensionalchemical structureofodorantsto the multidimensionalspacetimeof humanvisual field, through the bidimensio... | https://en.wikipedia.org/wiki/Receptive_field |
Image stitchingorphoto stitchingis the process of combining multiplephotographicimageswith overlapping fields of view to produce a segmentedpanoramaor high-resolution image. Commonly performed through the use ofcomputer software, most approaches to image stitching require nearly exact overlaps between images and identi... | https://en.wikipedia.org/wiki/Image_stitching |
Scale-spacetheory is a framework formulti-scalesignalrepresentationdeveloped by thecomputer vision,image processingandsignal processingcommunities with complementary motivations fromphysicsandbiological vision. It is a formal theory for handling image structures at differentscales, by representing an image as a one-par... | https://en.wikipedia.org/wiki/Scale_space |
In the areas ofcomputer vision,image analysisandsignal processing, the notion of scale-space representation is used for processing measurement data at multiple scales, and specifically enhance or suppress image features over different ranges of scale (see the article onscale space). A special type of scale-space repres... | https://en.wikipedia.org/wiki/Scale_space_implementation |
Structure from motion(SfM)[1]is aphotogrammetricrange imagingtechnique for estimating three-dimensional structures from two-dimensional image sequences that may be coupled with localmotion signals. It is a classic problem studied in the fields ofcomputer visionandvisual perception. In computer vision, the problem of Sf... | https://en.wikipedia.org/wiki/Structure_from_motion |
Zero ASIC Corporation, formerlyAdapteva, Inc., is afablesssemiconductorcompanyfocusing on low powermany coremicroprocessordesign. The company was the second company to announce a design with 1,000 specialized processing cores on a singleintegrated circuit.[1][2]
Adapteva was founded in 2008 with the goal of bringing a... | https://en.wikipedia.org/wiki/Adapteva_Epiphany |
TheCell Broadband Engine(Cell/B.E.) is a 64-bitmulti-core processorandmicroarchitecturedeveloped bySony,Toshiba, andIBM—an alliance known as "STI". It combines a general-purposePowerPCcore, called the Power Processing Element (PPE), with multiple specializedcoprocessors, known as Synergistic Processing Elements (SPEs),... | https://en.wikipedia.org/wiki/CELL |
Agraphics processing unit(GPU) is a specializedelectronic circuitdesigned fordigital image processingand to acceleratecomputer graphics, being present either as a discretevideo cardor embedded onmotherboards,mobile phones,personal computers,workstations, andgame consoles. GPUs were later found to be useful for non-grap... | https://en.wikipedia.org/wiki/Graphics_processing_unit |
Asystem on a chip(SoC) is anintegrated circuitthat combines most or all key components of acomputerorelectronic systemonto a single microchip.[1]Typically, an SoC includes acentral processing unit(CPU) withmemory,input/output, anddata storagecontrol functions, along with optional features like agraphics processing unit... | https://en.wikipedia.org/wiki/MPSoC |
OpenVXis an open, royalty-free standard for cross-platform acceleration ofcomputer visionapplications. It is designed by theKhronos Groupto facilitate portable, optimized and power-efficient processing of methods for vision algorithms. This is aimed forembeddedandreal-timeprograms within computer vision and related sce... | https://en.wikipedia.org/wiki/OpenVX |
Aphysics processing unit(PPU) is a dedicatedmicroprocessordesigned to handle the calculations ofphysics, especially in thephysics engineofvideo games. It is an example ofhardware acceleration.
Examples of calculations involving a PPU might includerigid body dynamics,soft body dynamics,collision detection,fluid dynamic... | https://en.wikipedia.org/wiki/Physics_processing_unit |
Theinformation ratiomeasures and compares theactive returnof an investment (e.g., a security or portfolio) compared to a benchmark index relative to the volatility of the active return (also known asactive riskorbenchmark tracking risk). It is defined as theactive return(the difference between the returns of the inves... | https://en.wikipedia.org/wiki/Information_ratio |
Instatistics, thevariance functionis asmooth functionthat depicts thevarianceof arandom quantityas a function of itsmean. The variance function is a measure ofheteroscedasticityand plays a large role in many settings of statistical modelling. It is a main ingredient in thegeneralized linear modelframework and a tool us... | https://en.wikipedia.org/wiki/Variance_function |
Modern portfolio theory(MPT), ormean-variance analysis, is a mathematical framework for assembling a portfolio of assets such that theexpected returnis maximized for a given level of risk. It is a formalization and extension ofdiversificationin investing, the idea that owning different kinds of financial assets is less... | https://en.wikipedia.org/wiki/Modern_portfolio_theory |
Simply stated,post-modern portfolio theory(PMPT) is an extension of the traditionalmodern portfolio theory(MPT) of Markowitz and Sharpe. Both theories provide analytical methods for rational investors to use diversification to optimize their investment portfolios. The essential difference between PMPT and MPT is that P... | https://en.wikipedia.org/wiki/Post-modern_portfolio_theory |
In finance, theSharpe ratio(also known as theSharpe index, theSharpe measure, and thereward-to-variability ratio) measures the performance of an investment such as asecurityorportfoliocompared to arisk-free asset, after adjusting for itsrisk. It is defined as the difference between the returns of the investment and the... | https://en.wikipedia.org/wiki/Sharpe_ratio |
TheSortino ratiomeasures therisk-adjusted returnof an investmentasset,portfolio, orstrategy.[1]It is a modification of theSharpe ratiobut penalizes only those returns falling below a user-specified target or requiredrate of return, while the Sharpe ratio penalizes both upside and downsidevolatilityequally. Though both ... | https://en.wikipedia.org/wiki/Sortino_ratio |
Theupside-potential ratiois a measure of a return of an investment asset relative to the minimal acceptable return. The measurement allows a firm or individual to choose investments which have had relatively good upside performance, per unit ofdownside risk.
where the returnsRr{\displaystyle R_{r}}have been put into i... | https://en.wikipedia.org/wiki/Upside_potential_ratio |
Instatistics, astandard normal table, also called theunit normal tableorZ table,[1]is amathematical tablefor the values ofΦ, thecumulative distribution functionof thenormal distribution. It is used to find theprobabilitythat astatisticis observed below, above, or between values on the standard normal distribution, and... | https://en.wikipedia.org/wiki/Standard_normal_table |
Instatistics,Cook's distanceorCook'sDis a commonly used estimate of theinfluenceof a data point when performing a least-squaresregression analysis.[1]In a practicalordinary least squaresanalysis, Cook's distance can be used in several ways: to indicate influential data points that are particularly worth checking for va... | https://en.wikipedia.org/wiki/Cook%27s_distance |
In statistics,Grubbs's testor theGrubbs test(named afterFrank E. Grubbs, who published the test in 1950[1]), also known as themaximum normalizedresidualtestorextreme studentized deviate test, is atestused to detectoutliersin aunivariatedata setassumed to come from anormally distributedpopulation.
Grubbs's test is base... | https://en.wikipedia.org/wiki/Grubbs%27s_test |
Instatistics,Samuelson's inequality, named after the economistPaul Samuelson,[1]also called theLaguerre–Samuelson inequality,[2][3]after the mathematicianEdmond Laguerre, states that every one of any collectionx1, ...,xn, is within√n− 1uncorrected samplestandard deviationsof their sample mean.
If we let
be the sample... | https://en.wikipedia.org/wiki/Samuelson%27s_inequality |
William Sealy Gosset(13 June 1876 – 16 October 1937) was an English statistician, chemist and brewer who worked forGuinness. In statistics, he pioneered small sample experimental design. Gosset published under thepen nameStudentand developedStudent's t-distribution– originally called Student's "z" – and "Student's test... | https://en.wikipedia.org/wiki/William_Sealy_Gosset |
Thenormal probability plotis agraphical techniqueto identify substantive departures fromnormality. This includes identifyingoutliers,skewness,kurtosis, a need for transformations, andmixtures. Normal probability plots are made of raw data,residuals from model fits, and estimated parameters.
In a normal probability p... | https://en.wikipedia.org/wiki/Normal_probability_plot |
In statistics, aQ–Q plot(quantile–quantile plot) is a probability plot, agraphical methodfor comparing twoprobability distributionsby plotting theirquantilesagainst each other.[1]A point(x,y)on the plot corresponds to one of the quantiles of the second distribution (y-coordinate) plotted against the same quantile of th... | https://en.wikipedia.org/wiki/Q%E2%80%93Q_plot |
Inprobability theoryandstatistics, there are several relationships amongprobability distributions. These relations can be categorized in the following groups:
Multiplying the variable by any positive real constant yields ascalingof the original distribution.
Some are self-replicating, meaning that the scaling yields t... | https://en.wikipedia.org/wiki/Relationships_among_probability_distributions |
Aproduct distributionis aprobability distributionconstructed as the distribution of theproductofrandom variableshaving two other known distributions. Given twostatistically independentrandom variablesXandY, the distribution of the random variableZthat is formed as the productZ=XY{\displaystyle Z=XY}is aproduct distribu... | https://en.wikipedia.org/wiki/Product_distribution |
Theratio estimatoris astatistical estimatorfor theratioofmeansof two random variables. Ratio estimates arebiasedand corrections must be made when they are used in experimental or survey work. The ratio estimates are asymmetrical and symmetrical tests such as thet testshould not be used to generate confidence intervals.... | https://en.wikipedia.org/wiki/Ratio_estimator |
Inmachine learning,normalizationis a statistical technique with various applications. There are two main forms of normalization, namelydata normalizationandactivation normalization. Data normalization (orfeature scaling) includes methods that rescale input data so that thefeatureshave the same range, mean, variance, or... | https://en.wikipedia.org/wiki/Normalization_(machine_learning) |
Insignal processing,Feature space Maximum Likelihood Linear Regression(fMLLR) is a global feature transform that are typically applied in a speaker adaptive way, where fMLLR transforms acoustic features to speaker adapted features by a multiplication operation with a transformation matrix. In some literature, fMLLR is ... | https://en.wikipedia.org/wiki/FMLLR |
Ahyper-heuristicis aheuristicsearch method that seeks to automate, often by the incorporation ofmachine learningtechniques, the process of selecting, combining, generating or adapting several simpler heuristics (or components of such heuristics) to efficiently solve computational search problems. One of the motivations... | https://en.wikipedia.org/wiki/Hyper-heuristic |
The replication crisis, also known as the reproducibility or replicability crisis, refers to the growing number of published scientific results that other researchers have been unable to reproduce or verify. Because the reproducibility of empirical results is an essential part of thescientific method,[2]such failures u... | https://en.wikipedia.org/wiki/Replication_crisis |
Limited-memory BFGS(L-BFGSorLM-BFGS) is anoptimizationalgorithmin the family ofquasi-Newton methodsthat approximates theBroyden–Fletcher–Goldfarb–Shanno algorithm(BFGS) using a limited amount ofcomputer memory.[1]It is a popular algorithm for parameter estimation inmachine learning.[2][3]The algorithm's target problem ... | https://en.wikipedia.org/wiki/Orthant-wise_limited-memory_quasi-Newton |
In numerical analysis,Broyden's methodis aquasi-Newton methodforfinding rootsinkvariables. It was originally described byC. G. Broydenin 1965.[1]
Newton's methodfor solvingf(x) =0uses theJacobian matrix,J, at every iteration. However, computing this Jacobian can be a difficult and expensive operation; for large proble... | https://en.wikipedia.org/wiki/Broyden%27s_method |
TheDavidon–Fletcher–Powell formula(orDFP; named afterWilliam C. Davidon,Roger Fletcher, andMichael J. D. Powell) finds the solution to the secant equation that is closest to the current estimate and satisfies the curvature condition. It was the firstquasi-Newton methodto generalize thesecant methodto a multidimensional... | https://en.wikipedia.org/wiki/DFP_updating_formula |
TheSymmetric Rank 1(SR1) method is aquasi-Newton methodto update the second derivative (Hessian)
based on the derivatives (gradients) calculated at two points. It is a generalization to thesecant methodfor a multidimensional problem.
This update maintains thesymmetryof the matrix but doesnotguarantee that the update be... | https://en.wikipedia.org/wiki/SR1_formula |
Inmathematics, thespectral radiusof asquare matrixis the maximum of the absolute values of itseigenvalues.[1]More generally, the spectral radius of abounded linear operatoris thesupremumof the absolute values of the elements of itsspectrum. The spectral radius is often denoted byρ(·).
Letλ1, ...,λnbe the eigenvalues o... | https://en.wikipedia.org/wiki/Spectral_radius |
Legal informaticsis an area withininformation science.
TheAmerican Library Associationdefinesinformaticsas "the study of thestructureandpropertiesofinformation, as well as the application oftechnologyto theorganization,storage,retrieval, and dissemination of information." Legal informatics therefore, pertains to the a... | https://en.wikipedia.org/wiki/Applications_of_artificial_intelligence_to_legal_informatics |
Deep learningis a subset ofmachine learningthat focuses on utilizing multilayeredneural networksto perform tasks such asclassification,regression, andrepresentation learning. The field takes inspiration frombiological neuroscienceand is centered around stackingartificial neuronsinto layers and "training" them to proces... | https://en.wikipedia.org/wiki/Applications_of_deep_learning |
Machine learning(ML) is afield of studyinartificial intelligenceconcerned with the development and study ofstatistical algorithmsthat can learn fromdataandgeneraliseto unseen data, and thus performtaskswithout explicitinstructions.[1]Within a subdiscipline in machine learning, advances in the field ofdeep learninghave ... | https://en.wikipedia.org/wiki/Applications_of_machine_learning |
Collective intelligence(CI) is shared orgroupintelligence(GI) thatemergesfrom thecollaboration, collective efforts, and competition of many individuals and appears inconsensus decision making. The term appears insociobiology,political scienceand in context of masspeer reviewandcrowdsourcingapplications. It may involvec... | https://en.wikipedia.org/wiki/Collective_intelligence#Applications |
Open dataaredatathat are openly accessible, exploitable, editable and shareable by anyone for any purpose. Open data are generally licensed under anopen license.[1][2][3]
The goals of the open data movement are similar to those of other "open(-source)" movements such as open-source software,open-source hardware,open c... | https://en.wikipedia.org/wiki/Open_data |
Progress in artificial intelligence(AI) refers to the advances, milestones, and breakthroughs that have been achieved in the field ofartificial intelligenceover time. AI is a multidisciplinary branch ofcomputer sciencethat aims to create machines and systems capable of performing tasks that typically require human inte... | https://en.wikipedia.org/wiki/Progress_in_artificial_intelligence |
This article presents a detailedtimelineof events in the history ofcomputingfrom 2020 to the present. For narratives explaining the overall developments, see thehistory of computing.
Significant events incomputinginclude events relating directly or indirectly tosoftware,hardwareandwetware.
Excluded (except in instance... | https://en.wikipedia.org/wiki/Timeline_of_computing_2020%E2%80%93present |
The following table comparescognitive architectures. | https://en.wikipedia.org/wiki/Comparison_of_cognitive_architectures |
Incommunications technology, the technique ofcompressed sensing(CS) may be applied tothe processing of speech signalsunder certain conditions. In particular, CS can be used to reconstruct asparse vectorfrom a smaller number of measurements, provided the signal can be represented in sparsedomain. "Sparse domain" refers ... | https://en.wikipedia.org/wiki/Compressed_sensing_in_speech_signals |
Noiseletsare functions which gives the worst case behavior for theHaar waveletpacket analysis. In other words, noiselets are totally incompressible by the Haar wavelet packet analysis.[1]Like the canonical and Fourier bases, which have an incoherent property, noiselets are perfectly incoherent with the Haar basis. In a... | https://en.wikipedia.org/wiki/Noiselet |
Sparse approximation(also known assparse representation) theory deals withsparsesolutions forsystems of linear equations. Techniques for finding these solutions and exploiting them in applications have found wide use inimage processing,signal processing,machine learning,medical imaging, and more.
Consider alinear syst... | https://en.wikipedia.org/wiki/Sparse_approximation |
Verification-based message-passingalgorithms(VB-MPAs)incompressed sensing(CS), a branch ofdigital signal processingthat deals with measuringsparse signals, are some methods to efficiently solve the recovery problem in compressed sensing. One of the main goal in compressed sensing is the recovery process. Generally spea... | https://en.wikipedia.org/wiki/Verification-based_message-passing_algorithms_in_compressed_sensing |
The following tables compare notablesoftware frameworks,libraries, andcomputer programsfordeep learningapplications.
[further explanation needed] | https://en.wikipedia.org/wiki/Comparison_of_deep-learning_software |
Extreme learning machinesarefeedforward neural networksforclassification,regression,clustering,sparse approximation, compression andfeature learningwith a single layer or multiple layers of hidden nodes, where the parameters of hidden nodes (not just the weights connecting inputs to hidden nodes) need to be tuned. Thes... | https://en.wikipedia.org/wiki/Extreme_learning_machine |
Inimaging science,difference of Gaussians(DoG) is afeatureenhancement algorithm that involves the subtraction of oneGaussian blurredversion of an original image from another, less blurred version of the original. In the simple case ofgrayscale images, the blurred images are obtained byconvolvingthe originalgrayscale im... | https://en.wikipedia.org/wiki/Difference_of_Gaussians |
Inmathematics, aGaussian function, often simply referred to as aGaussian, is afunctionof the base formf(x)=exp(−x2){\displaystyle f(x)=\exp(-x^{2})}and with parametric extensionf(x)=aexp(−(x−b)22c2){\displaystyle f(x)=a\exp \left(-{\frac {(x-b)^{2}}{2c^{2}}}\right)}for arbitraryrealconstantsa,band non-zeroc. It is na... | https://en.wikipedia.org/wiki/Gaussian_function |
Incomputer graphics,mipmaps(alsoMIP maps) orpyramids[1][2][3]are pre-calculated,optimizedsequences ofimages, each of which is a progressively lowerresolutionrepresentation of the previous. The height and width of each image, or level, in the mipmap is a factor of two smaller than the previous level. Mipmaps do not have... | https://en.wikipedia.org/wiki/Mipmap |
Biological neuron models, also known asspikingneuron models,[1]are mathematical descriptions of the conduction of electrical signals inneurons. Neurons (or nerve cells) areelectrically excitablecells within thenervous system, able to fire electric signals, calledaction potentials, across aneural network.These mathemati... | https://en.wikipedia.org/wiki/Biological_neuron_model |
Theunity of consciousness and(cognitive)binding problemis the problem of how objects, background, and abstract or emotional features are combined into a single experience.[1]The binding problem refers to the overall encoding of our brain circuits for the combination of decisions, actions, and perception. It is consider... | https://en.wikipedia.org/wiki/Binding_problem |
Acognitive mapis a type ofmental representationused by an individual to order their personal store of information about their everyday or metaphorical spatial environment, and the relationship of its component parts. The concept was introduced byEdward Tolmanin 1948.[1]He tried to explain the behavior of rats that app... | https://en.wikipedia.org/wiki/Cognitive_map |
Feature integration theoryis a theory ofattentiondeveloped in 1980 byAnne Treismanand Garry Gelade that suggests that when perceiving a stimulus, features are "registered early, automatically, and in parallel, while objects are identified separately" and at a later stage in processing. The theory has been one of the mo... | https://en.wikipedia.org/wiki/Feature_integration_theory |
Thegrandmother cell, sometimes called the "Jennifer Anistonneuron", is a hypotheticalneuronthat represents a complex but specific concept or object.[1]It activates when a person "sees, hears, or otherwise sensibly discriminates"[2]a specific entity, such as their grandmother. It contrasts with the concept ofensemble co... | https://en.wikipedia.org/wiki/Grandmother_cell |
Models of neural computationare attempts to elucidate, in an abstract and mathematical fashion, the core principles that underlie information processing in biological nervous systems, or functional components thereof. This article aims to provide an overview of the most definitive models of neuro-biological computation... | https://en.wikipedia.org/wiki/Models_of_neural_computation |
Theneural correlates ofconsciousness(NCC) are the minimal set of neuronal events and mechanisms sufficient for the occurrence of themental statesto which they are related.[2]Neuroscientistsuseempirical approachesto discoverneural correlatesof subjective phenomena; that is, neural changes which necessarily and regularly... | https://en.wikipedia.org/wiki/Neural_correlate |
Neural decodingis aneurosciencefield concerned with the hypothetical reconstruction of sensory and other stimuli from information that has already been encoded and represented in thebrainbynetworksofneurons.[1]Reconstruction refers to the ability of the researcher to predict what sensory stimuli the subject is receivin... | https://en.wikipedia.org/wiki/Neural_decoding |
Neural oscillations, orbrainwaves, are rhythmic or repetitive patterns of neural activity in thecentral nervous system.Neural tissuecan generateoscillatory activityin many ways, driven either by mechanisms within individualneuronsor by interactions between neurons. In individual neurons, oscillations can appear either... | https://en.wikipedia.org/wiki/Neural_oscillation |
Inneuroscience,representational driftis a phenomenon describing the gradual change in how thebrainrepresents information over time, even when the information (and associated perception or behavior) itself remains constant. This contrasts with the idea of stableneural representations, where the same information would id... | https://en.wikipedia.org/wiki/Representational_drift |
Inmachine learning, aneural network(alsoartificial neural networkorneural net, abbreviatedANNorNN) is a computational model inspired by the structure and functions of biological neural networks.[1][2]
A neural network consists of connected units or nodes calledartificial neurons, which loosely model theneuronsin the b... | https://en.wikipedia.org/wiki/Criticism_of_artificial_neural_networks |
Deep learningis a subset ofmachine learningthat focuses on utilizing multilayeredneural networksto perform tasks such asclassification,regression, andrepresentation learning. The field takes inspiration frombiological neuroscienceand is centered around stackingartificial neuronsinto layers and "training" them to proces... | https://en.wikipedia.org/wiki/Criticism_of_deep_learning |
Criticism of Googleincludes concern fortax avoidance, misuse and manipulation ofsearch results, its use of others'intellectual property, concerns that its compilation of data may violate people'sprivacyand collaboration with the US military onGoogle Earthto spy on users,[1]censorship of search results and content, its ... | https://en.wikipedia.org/wiki/Criticism_of_Google |
Thecut-up technique(ordécoupéin French) is analeatorynarrative techniquein which a written text is cut up and rearranged to create a new text. The concept can be traced to theDadaistsof the 1920s, but it was developed and popularized in the 1950s and early 1960s, especially by writerWilliam Burroughs. It has since been... | https://en.wikipedia.org/wiki/Cut-up_technique |
Theinfinite monkey theoremstates that amonkeyhitting keys independently and atrandomon atypewriterkeyboard for aninfiniteamount of time willalmost surelytype any given text, including the complete works ofWilliam Shakespeare.[a]More precisely, under the assumption of independence and randomness of each keystroke, the m... | https://en.wikipedia.org/wiki/Infinite_monkey_theorem |
Generative artificial intelligence(Generative AI,GenAI,[1]orGAI) is a subfield ofartificial intelligencethat uses generative models to produce text, images, videos, or other forms of data.[2][3][4]These modelslearnthe underlying patterns and structures of theirtraining dataand use them to produce new data[5][6]based on... | https://en.wikipedia.org/wiki/Generative_AI |
Mark V. Shaneyis a syntheticUsenetuser whose postings in thenet.singlesnewsgroupswere generated byMarkov chaintechniques, based on text from other postings. The username is a play on the words "Markov chain". Many readers were fooled into thinking that the quirky, sometimes uncannily topical posts were written by a r... | https://en.wikipedia.org/wiki/Mark_V._Shaney |
In probability theory and statistics, aMarkov chainorMarkov processis astochastic processdescribing asequenceof possible events in which theprobabilityof each event depends only on the state attained in the previous event. Informally, this may be thought of as, "What happens next depends only on the state of affairsnow... | https://en.wikipedia.org/wiki/Markov_text |
Instatistical decision theory, anadmissible decision ruleis arule for making a decisionsuch that there is no other rule that is always "better" than it[1](or at least sometimes better and never worse), in the precise sense of "better" defined below. This concept is analogous toPareto efficiency.
DefinesetsΘ{\displayst... | https://en.wikipedia.org/wiki/Admissible_decision_rule |
Instatistics,shrinkageis the reduction in the effects of sampling variation. Inregression analysis, a fitted relationship appears to perform less well on a new data set than on the data set used for fitting.[1]In particular the value of thecoefficient of determination'shrinks'. This idea is complementary tooverfittinga... | https://en.wikipedia.org/wiki/Shrinkage_estimator |
Regular estimatorsare a class ofstatistical estimatorsthat satisfy certain regularity conditions which make them amenable toasymptoticanalysis. The convergence of aregular estimator'sdistribution is, in a sense, locally uniform. This is often considered desirable and leads to the convenient property that a small change... | https://en.wikipedia.org/wiki/Regular_estimator |
Inmathematical statistics, theKullback–Leibler(KL)divergence(also calledrelative entropyandI-divergence[1]), denotedDKL(P∥Q){\displaystyle D_{\text{KL}}(P\parallel Q)}, is a type ofstatistical distance: a measure of how much a modelprobability distributionQis different from a true probability distributionP.[2][3]Mathem... | https://en.wikipedia.org/wiki/KL_divergence |
Theapproximation errorin a given data value represents the significant discrepancy that arises when an exact, true value is compared against someapproximationderived for it. This inherent error in approximation can be quantified and expressed in two principal ways: as anabsolute error, which denotes the direct numerica... | https://en.wikipedia.org/wiki/Percentage_error |
Instatisticsandsignal processing, aminimum mean square error(MMSE) estimator is an estimation method which minimizes themean square error(MSE), which is a common measure of estimator quality, of the fitted values of adependent variable. In theBayesiansetting, the term MMSE more specifically refers to estimation with qu... | https://en.wikipedia.org/wiki/Minimum_mean-square_error |
Squared deviations from the mean(SDM) result fromsquaringdeviations. Inprobability theoryandstatistics, the definition ofvarianceis either theexpected valueof the SDM (when considering a theoreticaldistribution) or its average value (for actual experimental data). Computations foranalysis of varianceinvolve the partiti... | https://en.wikipedia.org/wiki/Squared_deviations |
Inbioinformatics, theroot mean square deviation of atomic positions, or simplyroot mean square deviation (RMSD), is the measure of the average distance between the atoms (usually the backbone atoms) ofsuperimposedmolecules.[1]In the study of globular protein conformations, one customarily measures the similarity in thr... | https://en.wikipedia.org/wiki/Root-mean-square_deviation_of_atomic_positions |
Instatistics,Mallows'sCp{\textstyle {\boldsymbol {C_{p}}}},[1][2]named forColin Lingwood Mallows, is used to assess thefitof aregression modelthat has been estimated usingordinary least squares. It is applied in the context ofmodel selection, where a number ofpredictor variablesare available for predicting some outcome... | https://en.wikipedia.org/wiki/Mallows%27s_Cp |
Inestimation theoryanddecision theory, aBayes estimatoror aBayes actionis anestimatorordecision rulethat minimizes theposteriorexpected valueof aloss function(i.e., theposterior expected loss). Equivalently, it maximizes the posterior expectation of autilityfunction. An alternative way of formulating an estimator withi... | https://en.wikipedia.org/wiki/Bayesian_estimator |
Instatisticsandsignal processing, theorthogonality principleis anecessary and sufficientcondition for the optimality of aBayesian estimator. Loosely stated, the orthogonality principle says that the error vector of the optimal estimator (in amean square errorsense) is orthogonal to any possible estimator. The orthogona... | https://en.wikipedia.org/wiki/Orthogonality_principle |
Insignal processing, theWiener filteris afilterused to produce an estimate of a desired or target random process by linear time-invariant (LTI) filtering of an observed noisy process, assuming knownstationarysignal and noise spectra, and additive noise. The Wiener filter minimizes themean square errorbetween the estima... | https://en.wikipedia.org/wiki/Wiener_filter |
Instatisticsandcontrol theory,Kalman filtering(also known aslinear quadratic estimation) is analgorithmthat uses a series of measurements observed over time, includingstatistical noiseand other inaccuracies, to produce estimates of unknown variables that tend to be more accurate than those based on a single measurement... | https://en.wikipedia.org/wiki/Kalman_filter |
Linear predictionis a mathematical operation where future values of adiscrete-timesignalare estimated as alinear functionof previous samples.
Indigital signal processing, linear prediction is often calledlinear predictive coding(LPC) and can thus be viewed as a subset offilter theory. Insystem analysis, a subfield ofm... | https://en.wikipedia.org/wiki/Linear_prediction |
Thezero-forcing equalizeris a form of linearequalizationalgorithmused incommunication systemswhich applies the inverse of thefrequency responseof the channel. This form of equalizer was first proposed byRobert Lucky.
The zero-forcing equalizer applies the inverse of the channel frequency response to the received signa... | https://en.wikipedia.org/wiki/Zero-forcing_equalizer |
TheCzenakowski distance(sometimes shortened asCZD) is a per-pixel quality metric that estimates quality or similarity by measuring differences between pixels. Because it compares vectors with strictly non-negative elements, it is often used to compare colored images, as color values cannot be negative. This different a... | https://en.wikipedia.org/wiki/Czenakowski_distance |
Data compression ratio, also known ascompression power, is a measurement of the relative reduction in size of data representation produced by a data compression algorithm. It is typically expressed as the division of uncompressed size by compressed size.
Data compression ratio is defined as the ratio between theuncomp... | https://en.wikipedia.org/wiki/Data_compression_ratio |
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