Negative relationship - A negative relationship is an association in which larger values of one variable tend to accompany smaller values of another.
Negative sampling - Negative sampling is a training technique that contrasts observed positive examples with a selected set of negative examples instead of comparing against...
Negative testing - Negative testing checks how software behaves when it receives invalid inputs or encounters conditions outside its intended normal use.
Negatively skewed - A negatively skewed distribution is asymmetric with a tail extending farther toward lower values than toward higher values. It is also called left-skewed.
Neighbor - A neighbor of a node in a simple undirected graph is another node connected to it directly by an edge.
Neighborhood - A neighborhood is a collection of points or nodes treated as local to a reference point under a specified rule.
Neighborhood Components Analysis - Neighborhood Components Analysis is a metric-learning method that reshapes the feature space so that points of the same class end up close together: it...
Neighborhood operation - A neighborhood operation computes an output value at an image location using values from a surrounding set of input locations.
Nelson - Aalen estimator - The Nelson - Aalen estimator estimates a cumulative hazard function from time-to-event data.
Nemenyi test - The Nemenyi test is a rank-based multiple-comparison procedure.
Neocognitron - The neocognitron is a hierarchical neural-network model for visual pattern recognition introduced by Kunihiko Fukushima.
NER model - The NER model is a framework for assessing the accuracy of live subtitles, particularly subtitles produced through respeaking.
Nested case - control study - A nested case - control study selects cases and controls from a defined cohort.
Nested cross-validation - Nested cross-validation is an evaluation procedure that places a model-selection process inside an outer cross-validation process.
Nested hypothesis - Nested hypotheses form an ordered sequence in which every hypothesis permits only possibilities also permitted by each later hypothesis.
Nested Loop - A nested loop is a process in which an inner operation runs for each item handled by an outer operation.
Nested partition model - In the CrossCat-style setting used here, a nested partition model groups the columns of a data table into views and assigns a separate partition of the rows...
Nested Relation - A nested relation is a relation whose tuples can contain relation-valued attributes: a value can itself be a collection of structured records.
Nested sampling algorithm - Nested sampling is a numerical method for estimating Bayesian evidence and obtaining weighted posterior samples.
Nested sequential Monte Carlo - Nested sequential Monte Carlo, or NSMC, uses Monte Carlo procedures inside a sequential Monte Carlo algorithm to construct proposals that would otherwise be...
Nesterov accelerated gradient - Nesterov accelerated gradient, often abbreviated NAG, is a first-order optimization method that combines momentum with a gradient evaluated at an...
Net reclassification improvement - Net reclassification improvement, or NRI, compares how two risk-prediction models move observations upward or downward in predicted risk.
Network Data Envelopment Analysis - Network Data Envelopment Analysis, or network DEA, evaluates the relative efficiency of organizations while representing their internal stages and the flows...
Network Data Model - The network data model organizes database records through explicitly defined relationships that can form a network rather than a single tree.
Network Lasso - Network Lasso is an optimization framework that fits variables or local models at graph nodes while penalizing differences across connected nodes.
Network on a chip - A network on a chip, usually written network-on-chip or NoC, is a communication network inside an integrated circuit.
Network performance - Network performance describes how well a communication network carries data for a specified task and set of conditions.
Network sampling - Network sampling is a survey approach in which a target item can be reported through several linked sampling units.
Network Simplex - Network simplex is an optimization algorithm for finding a minimum-cost flow through a network while satisfying supply, demand, and capacity constraints.
Network throughput - Network throughput is the rate at which data passes through a network, link or device under specified conditions.
Network transparency - Network transparency is the property of a system that hides some details of network communication from its users or applications.
Networked data - Networked data are observations or local datasets accompanied by a network of relationships between them.
Networked exponential families - Networked exponential families are probabilistic models that assign an exponential-family distribution to each node of a graph and couple the node-level...
Networked federated learning - Networked federated learning is a family of methods that trains personalized local models from decentralized datasets while using a network of relationships...
Networked model - A networked model assigns a local hypothesis space to each node of a network used for federated learning.
Neumann boundary condition - A Neumann boundary condition specifies the normal derivative of a function at the edge of a domain.
Neural Architecture Search - Neural architecture search, or NAS, is the use of automated methods to explore neural-network designs and select promising architectures for a task.
Neural autoregressive density estimator - A neural autoregressive density estimator models a joint distribution as a product of conditional distributions predicted by neural networks.
Neural cryptography - Neural cryptography is research into the use of neural-network methods in cryptographic tasks and their analysis.
Neural field - In machine learning, a neural field is a representation of a quantity over coordinates in which a neural network supplies all or part of the mapping.
Neural gas - Neural gas is an unsupervised learning method that represents a data distribution using movable prototype vectors.
Neural-linear approximation - A neural-linear approximation combines a neural feature extractor with a Bayesian linear output model.
Neural machine translation - Neural machine translation, or NMT, uses neural networks to produce a translation from one human language into another.
Neural modeling fields - Neural modeling fields are a research framework associated with Leonid Perlovsky that links observed signals with adaptive internal models through gradually...
Neural network - A neural network is a computational model made from connected units that transform numerical information.
Neural network Gaussian process - A neural network Gaussian process, or NNGP, is a Gaussian-process model associated with the infinite-width limit of certain randomly initialized neural...
Neural network pruning - Neural network pruning removes or disables selected connections, parameters, or structural components of a neural network.
Neural operators - Neural operators are neural-network models designed to learn mappings between functions.