V-cycle - A V-cycle is a multigrid procedure that moves from a fine representation of a problem down through coarser levels and then returns to the fine level with...
V-trace - V-trace is an off-policy multi-step target construction for state-value learning, introduced with the IMPALA distributed actor-learner architecture.
VACUUM - VACUUM is a data-quality mnemonic referring to a Valid, Accurate, Consistent, Uniform and Unified Model.
Vague set - A vague set represents each element using separate degrees of evidence for membership and against membership.
Validation - Validation is the assessment of a model or system against criteria that help determine whether it is suitable for its purpose.
Validation error - Validation error is the average loss a trained model incurs on a validation dataset that is separate from the examples used to fit it.
Validation loss - Validation loss is the value of a specified loss function measured on validation data for a model checkpoint or candidate.
Validation set - A validation set is a collection of examples used to assess candidate models during development, separately from the examples used to fit each candidate.
Validity - In classical logic, validity is truth under every interpretation allowed by the logic.
Value alignment - Value alignment concerns whether an AI system's objectives and behavior correspond to the human values, interests, and intentions it is meant to serve.
Value-Based Identity - Value-based identity distinguishes entities using selected attribute values that carry meaning in the modeled domain.
Value imputation - Value imputation replaces missing data values with chosen or estimated substitutes.
Value iteration - Value iteration is a dynamic-programming method for finding optimal state values in a Markov decision process.
Value of perfect information - The value of perfect information is the expected improvement in the best achievable utility from learning an uncertain variable's exact value before making...
Value sensitive design - Value sensitive design is an approach to technology design that systematically considers human values throughout the design process.
Value symmetry - Value symmetry is a symmetry of a constraint problem that changes assigned values while leaving the variables in place and preserves solutions.
ValueDICE - ValueDICE is an imitation-learning algorithm that learns a policy by matching its discounted state-action distribution to an expert's distribution through...
Van der Waerden test - The Van der Waerden test is a rank-based test that compares independent groups by converting pooled ranks into standard normal quantiles, called normal scores.
Vanishing gradient - A vanishing gradient is a training signal that becomes very small as derivatives are propagated backward through many computational steps.
Vanishing point - A vanishing point is the image of a spatial direction under perspective projection.
Variable - A variable is a symbol or named quantity that can take values from a specified set, called its domain.
Variable bitrate - Variable bitrate, or VBR, is an encoding approach in which the amount of data used per unit of media time can change.
Variable byte encoding - Variable byte encoding represents an integer using one or more bytes, reserving a bit in each byte to mark whether the number continues.
Variable elimination - Variable elimination is a method for computing with a factored model by removing variables one at a time while preserving their effect on the remaining...
Variable importances - Variable importances are scores that describe how input variables matter to a fitted model according to a particular assessment method.
Variable kernel density estimation - Variable kernel density estimation estimates a probability density using smoothing kernels whose bandwidths vary across observations or locations.
Variable-length encoding - Definition: Variable-length encoding represents different items using code sequences of different lengths.
Variable neighborhood search - Variable neighborhood search (VNS) is an optimization framework that changes the kinds or sizes of moves used to explore candidate solutions.
Variable-order Bayesian network - A variable-order Bayesian network is a probabilistic model in which the conditioning information needed for a variable can depend on the observed context.
Variable-order Markov model - A variable-order Markov model predicts the next symbol in a sequence using a context whose length depends on the recent history.
Variable sampling fraction - A variable sampling fraction means that different proportions of units are selected from different parts of a population, commonly from different strata.
Variables sampling plan - A variables sampling plan is an acceptance-sampling procedure that uses quantitative measurements from sampled items to decide whether to accept a lot.
Variance - Variance measures spread by averaging squared deviations from a mean.
Variance components - Variance components are parameters that represent distinct sources of random variation in a statistical model.
Variance estimation - Variance estimation is the task of estimating the variability of a statistical estimator across repeated samples or repeated realizations of its...
Variance inflation factor ( VIF ) - The variance inflation factor, or VIF, measures how linear relationships among predictors inflate the variance of an estimated ordinary least squares...
Variance Sum Law - The variance sum law gives the variance of a sum or difference of random variables, including their covariance.
Variational approximation - A variational approximation replaces a difficult target distribution with a manageable distribution selected by optimization.
Variational autoencoder - A variational autoencoder, or VAE, is a probabilistic generative model that learns a latent-variable representation of data together with an approximate...
Variational Bayes expectation maximization - Variational Bayes expectation maximization, often called variational Bayesian EM or VBEM, is an iterative approach to approximate Bayesian inference that...
Variational Bayesian methods - Variational Bayesian methods approximate a Bayesian posterior by choosing a tractable probability distribution from a specified family and optimizing it to...
Variational bound integration - Variational bound integration approximates a difficult integral by replacing its integrand with a tractable lower bound and integrating that bound.
Variational bound optimization - Variational bound optimization improves a difficult objective by working with a more tractable bound on it.
Variational expectation maximization - Variational expectation maximization, or variational EM, is an alternating optimization method for latent-variable models when exact posterior inference is...
Variational filtering sequential Monte Carlo - Variational filtering sequential Monte Carlo refers here to combining particle-based sequential inference with variational optimization of proposals or...
Variational lower bound - A variational lower bound is an expression, controlled by adjustable auxiliary choices, that does not exceed a target quantity.
Variational message passing - Variational message passing, or VMP, is a way to organize variational inference as local computations and messages in a probabilistic graphical model.
Variational parameters - Variational parameters are adjustable quantities that specify an approximate distribution used in variational inference.