Lapped transform - A lapped transform represents a signal using transform functions whose support overlaps neighboring processing blocks.
Large language model - A large language model, or LLM, is a machine-learning model trained on extensive language data to learn patterns that help it process or generate language.
Large margin nearest neighbor - Large margin nearest neighbor, or LMNN, is a supervised metric-learning method that learns a distance transformation for nearest-neighbor classification.
Large memory storage and retrieval neural network - A large memory storage and retrieval neural network usually refers to LAMSTAR, a historical neural architecture that combines modules based on...
Large-scale learning - Large-scale learning is machine learning in a setting where the amount of data, size of the model, or computational workload makes time, memory, or data...
Large width limits of neural networks - Large width limits of neural networks are mathematical descriptions of what happens when the number of units or channels in selected layers grows without...
LARS Lasso - LARS Lasso is the lasso fitted by the Least Angle Regression algorithm: an efficient method that traces the whole path of models as the penalty relaxes...
Lasso - Lasso is regression with an L1 penalty, famous for driving the weights of unneeded fields to exactly zero, so fitting the model and selecting its fields...
Late Binding - Late binding means deferring a binding decision until a program runs.
Late move reduction - Late move reduction is a selective game-search technique that initially examines some later-ranked moves to a shallower depth than the main search.
Latency - Latency is the elapsed time between a defined initiating event and a defined response or completion event.
Latent class model - A latent class model represents a population as a mixture of unobserved categories, using patterns in observed variables to estimate those categories and...
Latent coincidence analysis - Latent coincidence analysis (LCA) is a supervised latent-variable method for learning a linear transformation in which distances reflect labeled similarity...
Latent diffusion - Latent diffusion is a generative modeling approach that performs a diffusion process in a learned latent representation instead of directly in the original...
Latent semantic analysis - Latent semantic analysis, or LSA, represents relationships among terms and documents by reducing a term - document matrix to a smaller set of numerical...
Latent semantic mapping - Latent semantic mapping, or LSM, is a framework for representing discrete items as vectors in a smaller continuous space, using patterns across a collection...
Latent variable - A latent variable is a variable included in a model but not directly observed in the available data.
Latent variable model - A latent variable model describes observed data using additional variables that are not directly observed in the dataset.
Lateral sound - A lateral sound is a speech sound produced with airflow passing around one or both sides of the tongue rather than through an open central channel.
Latin hypercube design - A Latin hypercube design is a sampling arrangement that spreads a chosen number of runs across every input variable's range or probability distribution.
Latin hypercube sampling - Latin hypercube sampling is a method for selecting combinations of input values so that each input's range is represented evenly through equal-probability...
Latin square - A Latin square is an n-by-n array filled with n distinct symbols so that each symbol appears exactly once in every row and exactly once in every column.
Latin square design - A Latin square design is an experimental arrangement that compares n treatments while blocking two nuisance factors, each having n levels.
Law of large numbers - The law of large numbers describes how an average of many observations approaches an expected value under specified assumptions.
Layer - A layer is a computational component of a neural network that transforms one or more inputs into outputs.
Layer norm - Layer norm, short for layer normalization, is a neural-network operation that normalizes activation values across specified feature dimensions within an...
Layered coding - Layered coding represents information through a base layer and one or more additional layers that provide further detail or capability when decoded with the...
Layered queueing network - A layered queueing network is a performance model that represents software services and the resources they use across interacting layers.
Lazy learning - Lazy learning is a learning approach that postpones substantial generalization until a prediction or query is requested.
LDA-HMM - LDA-HMM is a composite text model combining latent Dirichlet allocation's document-level topics with a hidden Markov model's local sequence structure.
LDL, lower detection limit - LDL, or lower detection limit, describes a measurement method's ability to distinguish a low-level analyte or signal from background under stated conditions.
Leabra - Leabra is a computational neural-modeling framework that combines error-driven and associative learning with biologically motivated activation and...
Leaf - A leaf is an end node of a decision tree: the terminal box where a case finally lands, carrying the tree's prediction for every case that arrives there.
Leak node - A leak node is an additional, usually implicit, cause in a probabilistic model that accounts for an effect occurring through causes not represented...
Lean integration - Lean integration applies lean improvement principles to the continuing work of connecting data, applications and business processes.
Leapfrog integrator - The leapfrog integrator is a numerical method that advances position and momentum through alternating half and full steps.
Learnable evolution model - The learnable evolution model, or LEM, is an evolutionary optimization approach that uses machine learning to generate promising new candidate solutions.
Learnable function class - A learnable function class is a set of candidate functions for which a learning procedure can achieve a specified generalization guarantee from data under...
Learned operator - In computational photography, a learned operator is a function whose behavior is fitted from data to perform an image-processing or prediction task.
Learned sparse retrieval - Learned sparse retrieval is a family of search methods that train models to produce sparse representations for matching queries and documents.
Learning - Learning, in AI, is the process through which a system uses experience or data to develop or improve its ability to perform a task.
Learning automaton - A learning automaton is an adaptive decision mechanism that selects actions, receives feedback from an environment and updates how it chooses future actions.
Learning curve - A learning curve shows how a learning system's measured performance changes as a relevant resource or amount of experience increases.
Learning element - A learning element is the part of a learning-agent architecture that changes the agent's decision-making components using experience and feedback.
Learning rate - Learning rate is the step-size dial of iterative training: how large an adjustment the model makes to its parameters at each update, trading speed of...
Learning rule - A learning rule is a procedure that specifies how a model changes its adjustable parameters or internal state in response to data or feedback.
Learning task - A learning task is the problem a machine-learning system is asked to learn from data.