Goodnessoffit test - A goodness-of-fit test evaluates whether observed data are sufficiently inconsistent with a specified statistical model to provide evidence against it.
GOP (group of pictures) - A GOP, or group of pictures, is a grouping or prediction structure within compressed video, commonly organized around successive keyframe or refresh points.
Gorn address - A Gorn address identifies a node in an ordered rooted tree by recording the sequence of child choices needed to reach it from the root.
Gossip protocol - A gossip protocol spreads information through repeated exchanges among nodes in a network, often with peers selected partly at random.
Gradient - The gradient of a differentiable scalar-valued function is the vector of its partial derivatives.
Gradient accumulation - Gradient accumulation is a training technique that combines gradients from several smaller batches before performing one optimizer update.
Gradient bandit algorithm - A gradient bandit algorithm learns action preferences and converts them into action-selection probabilities, typically with a softmax function.
Gradient-based method - A gradient-based method is an optimization procedure that uses derivatives of an objective to guide its search.
Gradient boosted (decision) trees - Gradient boosted decision trees are an ensemble model built by adding decision trees in stages, with each new tree trained to improve the current ensemble...
Gradient boosting - Gradient boosting is a modeling method that builds a strong predictor from many small decision trees added one at a time, each new tree trained to correct...
Gradient Checkpointing - Gradient checkpointing, also commonly called activation checkpointing, is a training technique that reduces stored intermediate activations by recomputing...
Gradient clipping - Gradient clipping is a training technique that limits the magnitude of gradients before an optimizer uses them to update model parameters.
Gradient descent - Gradient descent is an iterative method for minimizing a differentiable function.
Gradient domain - The gradient domain is a way of working with an image through its local changes in value rather than directly through its absolute pixel values.
Gradient reversal - Gradient reversal is an image-processing artifact in which a local brightness change reverses direction after processing: a region that originally became...
Gradient step - A gradient step is one update of a variable using the gradient of an objective.
Gradient temporal-difference learning - Gradient temporal-difference learning refers to methods that estimate value functions by following gradients of carefully defined temporal-difference...
Graduated optimization - Graduated optimization tackles a difficult objective through a sequence of simpler approximations.
Grafting (decision trees) - Grafting is a decision-tree learning technique that adds branches to an already constructed tree to improve its classifications.
Gram matrix - A Gram matrix records the pairwise inner products of a set of vectors.
Grammar - A grammar is a formal description of how valid expressions can be structured in a language.
Grammar-based code - A grammar-based code represents data using a formal grammar that reconstructs the original sequence through reusable rules.
Grammar induction - Grammar induction is the process of learning a grammatical representation or structural regularities from examples.
Grammar systems theory - Grammar systems theory studies collections of formal grammars that cooperate or communicate to generate formal languages.
Grammatical function - Grammatical function is the syntactic role an expression has within a larger construction, such as subject, object, complement, or modifier.
Grammatical relation - A grammatical relation is a syntactic relationship between an expression and another part of a sentence, such as the relationship of a subject or object to...
Granger causality - Granger causality is a predictive relationship between time series: the past of one series helps predict another beyond the information already included in...
Granular computing - Granular computing is an approach to representing and solving problems using meaningful groups or units of information called granules.
Graph - A graph is a mathematical structure made of vertices, also called nodes, and edges that represent relationships between them.
Graph clustering - Graph clustering groups data points represented as nodes of a graph using the relationships encoded by its edges.
Graph Coarsening Algorithm - A graph coarsening algorithm creates a smaller graph by combining selected vertices into groups and aggregating their connections.
Graph coloring - Graph coloring assigns labels, traditionally called colors, to graph elements under specified compatibility rules.
Graph Convolutional Network - A Graph Convolutional Network, or GCN, is a neural-network architecture that learns representations using a graph's connections together with features...
Graph Convolutional Policy Network - Graph Convolutional Policy Network, or GCPN, is a model introduced for goal-directed graph generation using reinforcement learning.
Graph cut optimization - Graph cut optimization solves certain labeling problems by constructing a weighted graph whose cuts correspond to candidate solutions.
Graph-cut segmentation - Graph-cut segmentation divides an image into labeled regions by constructing a weighted graph and finding a cut that minimizes a defined cost.
Graph entropy - In the information-theoretic sense associated with Körner, graph entropy measures a graph together with a probability distribution over its vertices.
Graph Hashing - Graph hashing converts a graph's selected structural and attribute information into a compact identifier.
Graph isomorphism - Graph isomorphism is a one-to-one correspondence between the vertices of two graphs that preserves their connections.
Graph kernel - A graph kernel is a function that compares two graphs in a way suitable for kernel-based learning, typically corresponding to an inner product between their...
Graph Language Model - A Graph Language Model combines language representations with information about graph structure, such as entities and their relationships.
Graph matching - Graph matching seeks a correspondence between vertices, edges or substructures of two graphs so that their attributes and relationships agree as well as the...
Graph neural network - A graph neural network, or GNN, is a neural model designed to learn from data represented as a graph of nodes and edges.
Graph of a function - The graph of a function is the set of all input-output pairs produced by that function over its specified domain.
Graph partitioning - Graph partitioning divides a graph's vertices into groups according to a defined objective and constraints.
Graph representation - A graph representation describes entities as vertices and relationships as edges, together with any relevant attributes.
Graph search - Graph search is a family of problem-solving procedures that explore states and transitions while accounting for states reached more than once.
Graph semantics - Graph semantics is the meaning carried by the structure of a graph: what the nodes represent, what the edges assert and how relationships between things...