Multiagent systems - Multiagent systems are systems in which several agents share an environment and interact: cooperating, competing or simply coexisting, each pursuing its own...
Multiarmed bandit - A multiarmed bandit is a sequential decision problem in which a learner repeatedly chooses among alternatives, called arms, and observes the reward of the...
Multiattribute utility theory - Multiattribute utility theory is a framework for representing preferences over outcomes described by several attributes, such as delivery speed...
Multiband blending - Multiband blending combines overlapping images by blending their information separately at different spatial frequency bands.
Multibody planning - Multibody planning is planning for several physically separate bodies, such as a fleet of robots, whose actions must be coordinated to achieve an overall...
Multiclass-multioutput classification - Multiclass-multioutput classification is the setting where each item must be assigned one class in each of several independent category systems at once: a...
Multicollinearity - Multicollinearity is a strong linear relationship among two or more predictor variables in a model.
Multicriteria classification - Multicriteria classification is the assignment of alternatives to predefined categories using several evaluation criteria.
Multidimensional analysis - Multidimensional analysis examines data through several variables, attributes or perspectives at once.
Multidimensional Scaling - Multidimensional scaling, or MDS, is a family of methods that represents objects as points so that distances between the points reflect supplied...
Multieffector planning - Multieffector planning is planning for a decision-making system with several effectors that can act, sometimes concurrently, on its environment.
Multifactor dimensionality reduction - Multifactor dimensionality reduction, or MDR, is a method for examining combinations of variables associated with a categorical outcome.
Multifidelity simulation - Multifidelity simulation combines models with different levels of detail, accuracy and computational cost to study the same system or quantity of interest.
Multigrid method - A multigrid method solves a numerical problem by combining calculations at several levels of resolution.
Multilabel indicator matrix - A multilabel indicator matrix is a two-dimensional representation of labels in which each row corresponds to a sample and each column corresponds to a...
Multilayer perceptron - A multilayer perceptron, or MLP, is a feedforward neural network built from layers of connected computational units.
Multilevel Monte Carlo method - The multilevel Monte Carlo method estimates an expected quantity by combining many inexpensive coarse simulations with fewer carefully paired corrections...
Multilinear principal component analysis - Multilinear principal component analysis is a dimensionality-reduction method that learns compact representations of tensor-shaped data through projections...
Multilinear subspace learning - Multilinear subspace learning is a family of methods that learn lower-dimensional representations directly from tensor-shaped observations.
Multilingual notation - Multilingual notation, in the lexical-resource sense used here, is a way of representing correspondences among lexical information in different languages.
Multimedia data - Multimedia data is information represented through media such as text, images, audio, video, graphics, and animation, especially when several forms are...
Multimodal Deep Learning - Multimodal deep learning uses deep neural networks to learn from relationships among different kinds of data, such as text, images, audio or sensor signals.
Multimodal instruction-tuned - Multimodal instruction-tuned describes a model trained to respond to instructions while processing inputs beyond text, such as images, audio, or video.
Multimodal Learning - Multimodal learning is machine learning that uses or relates information from different modalities, such as language, sound, images, touch or sensor...
Multimodal model - A multimodal model is a machine-learning model whose inputs, outputs, or both involve more than one type of information, such as text, images, audio, or video.
Multimodal Prompt-based Language Model - A multimodal prompt-based language model generates language in response to prompts containing more than one kind of information, such as text combined with...
Multimodal representation learning - Multimodal representation learning develops numerical representations from more than one kind of data, such as text, images, audio or video.
Multimodal sentiment analysis - Multimodal sentiment analysis estimates expressed positive, negative or other sentiment using more than one kind of input, such as words, vocal...
Multimodal Transformer - A multimodal Transformer is a model that uses Transformer-based processing to work with information from more than one modality, such as text and images, or...
Multimodal variational autoencoder - A multimodal variational autoencoder is a generative model that learns latent representations from more than one type of data, such as images, text, or...
Multinomial distribution - The multinomial distribution describes the counts of outcomes in a fixed number of independent trials when each trial falls into exactly one of several...
Multinomial logistic regression - Multinomial logistic regression is a statistical classification model for outcomes with more than two unordered categories.
Multinomial Naive Bayes - Multinomial Naive Bayes is a probabilistic classification method commonly used with nonnegative feature counts, especially word counts in text.
Multinomial probit - Multinomial probit is a statistical model for an outcome that takes one of several unordered categories, often interpreted as a choice among alternatives.
Multinomial resampling - Multinomial resampling replaces a weighted collection of particles with equally weighted copies selected randomly according to the original weights.
Multinomial test - A multinomial test evaluates whether observed category counts are compatible with a specified vector of category probabilities under a multinomial sampling...
Multioutput regression - Multioutput regression predicts two or more numerical target variables for each input example.
Multiple baseline design - A multiple baseline design is a single-case experimental design in which an intervention is introduced at different times across participants, behaviors or...
Multiple comparison techniques - Multiple comparison techniques are statistical procedures that account for making a family of comparisons rather than interpreting each comparison as if it...
Multiple comparisons - Multiple comparisons arise when an analysis examines several hypotheses, contrasts or statistical relationships and draws conclusions from the collection.
Multiple correlation coefficient - The multiple correlation coefficient, conventionally written R, measures how strongly an outcome is linearly associated with its best linear prediction from...
Multiple correspondence analysis - Multiple correspondence analysis is an exploratory method for summarizing associations among several categorical variables.
Multiple discriminant analysis - Multiple discriminant analysis uses several measured variables to distinguish among known groups, commonly by constructing linear combinations that...
Multiple inheritance - Multiple inheritance means that a class or category has more than one parent from which it inherits applicable characteristics.
Multiple instruction, single data - Multiple instruction, single data, usually abbreviated MISD, is a category in Flynn's taxonomy of computer architectures.
Multiple regression - Multiple regression models the relationship between one outcome and two or more predictors.