3,135 articles
Abstract argumentation framework
An abstract argumentation framework is a directed graph of arguments and attacks, introduced by Phan Minh Dung in 1995, where acceptability depends only on attack patterns.
Abstract data type
An abstract data type (ADT) is a mathematical model of a data type defined by the values it holds and its operations, independent of any particular implementation.
Abstract interpretation
Abstract interpretation is a theory of static analysis, founded by Patrick and Radhia Cousot in 1977, that proves program properties for all executions without running the program.
Abstract Meaning Representation
Abstract Meaning Representation (AMR) is a semantic representation language that encodes a sentence's meaning as a rooted, directed graph, capturing who is doing what to whom.
Abstract state machines
An abstract state machine is a formal method in computer science that models systems as transition rules over arbitrary mathematical states, used to design and verify algorithms.
AC0
AC0 is a circuit complexity class of constant-depth, polynomial-size Boolean circuits with unbounded fan-in AND, OR, and NOT gates, best known for the landmark lower bound that parity cannot be computed by such circuits.
Acceldata
Acceldata (Acceldata Inc), founded 2018 in Campbell, California by Rohit Choudhary and three Hortonworks colleagues, is a private data observability and agentic data management vendor; its ADOC and xLake platform raised about $105.6 million.
Accelerated gradient method
The accelerated gradient method, also called Nesterov's accelerated gradient descent, is a first-order optimization algorithm adding momentum to gradient descent, improving the convex convergence rate from O(1/n) to O(1/n²).
Accounting method (computer science)
The accounting method, also called the banker's method, is a technique of amortized analysis that overcharges operations and stores the excess as credit to pay for later expensive ones.
ACID
ACID, or atomicity, consistency, isolation, and durability, is a set of properties of database transactions guaranteeing valid results under concurrent operation and faults like power failures.
ACL (conference)
The Annual Meeting of the Association for Computational Linguistics (ACL) is the flagship conference of the Association for Computational Linguistics, covering computational linguistics and natural language processing since 1963.
Acoustic fingerprint
An acoustic fingerprint is a compact, content-based signature extracted from an audio signal, used to identify recordings against a database even when the query audio is noisy or degraded.
ACT-R
ACT-R, or Adaptive Control of Thought–Rational, is a cognitive architecture developed by John R. Anderson at Carnegie Mellon University, used to simulate and predict human behavior.
Activation function
An activation function is a fixed nonlinear function applied to each neuron's output in a neural network; without one, stacked layers collapse into a single linear map.
Active appearance model
An active appearance model (AAM) is a parametric computer vision model combining statistical shape and texture models, introduced in 1998 and applied widely to face alignment and medical image segmentation.
Active contour model
The active contour model, or snake, is an image segmentation method in which a curve deforms under energy minimization until it locks onto object boundaries.
Active learning (machine learning)
Active learning, also called pool-based active learning in its most studied form, is a machine learning approach where the algorithm chooses which unlabeled examples an oracle, usually a human annotator, should label.
Active metadata
Active metadata: metadata automatically collected and continuously updated in real time, then used to trigger actions, automate data management and governance. Contrasted with passive, Gartner-promoted enterprise practice.
Active site prediction
Active site prediction is a set of computational methods that identify the likely ligand-binding or catalytic regions of a protein from its sequence or structure.
Activity diagram
An activity diagram is a Unified Modeling Language (UML) behavioral diagram that models workflows and computations as a flow of actions, decisions, and parallel paths.
Activity schema
Activity Schema is an open, warehouse-native data modeling standard (spec 2.0, 2022) storing business data as one time-series activity stream table: entity, timestamp, activity, features; temporal self-joins, immutable activities, optional entity tables.
ActivityNet
ActivityNet is a large-scale video benchmark for human activity understanding introduced at CVPR 2015, with untrimmed YouTube videos and temporal annotations for classifying and localizing daily activities.
Actor-critic algorithm
The actor-critic algorithm is a reinforcement learning method combining a policy, the actor, with a value function, the critic, introduced by Barto, Sutton, and Anderson in 1983.
Actor-critic reinforcement learning
Actor-critic reinforcement learning is a machine learning method in which an actor parameterizes a policy and a critic estimates state values, combining low variance with direct policy optimization.
Ada Lovelace Institute
The Ada Lovelace Institute is an independent research institute in London, established by the Nuffield Foundation in 2018 to study the ethical and social implications of data and AI.
AdaBoost
AdaBoost is a machine-learning ensemble algorithm introduced by Yoav Freund and Robert Schapire that combines weak classifiers into a strong one; their 1997 paper won the 2003 Gödel Prize.
AdaDelta
AdaDelta is an adaptive learning rate method for gradient descent, proposed by Matthew D. Zeiler in December 2012, that eliminates the need for a hand-tuned global learning rate.
AdaGrad
AdaGrad is a gradient-based optimization algorithm that assigns each parameter its own learning rate, introduced by John Duchi, Elad Hazan, and Yoram Singer in 2010 for online learning.
ADALINE
ADALINE, short for ADaptive LInear NEuron, is an early artificial neuron introduced in 1960 by Bernard Widrow and Marcian Hoff at Stanford, trained by the Widrow-Hoff delta rule.
Adam optimizer
Adam, or adaptive moment estimation, is a first-order gradient-based optimization algorithm introduced by Diederik P. Kingma and Jimmy Ba in 2014, widely used for training deep learning models.