Artificial intelligence and data

Constraint propagation

Constraint propagation, also known as local consistency or filtering, is a family of polynomial-time algorithms for constraint satisfaction problems that remove values no solution can use.

Constraint satisfaction problem

A constraint satisfaction problem, also known as constraint solving, is an NP-complete computational problem of assigning values to variables under constraints, unifying satisfiability, graph coloring, and scheduling.

Content-based filtering

Content-based filtering is a recommendation method that matches item features, such as text, tags, or genres, against a profile of one user's past preferences, unlike collaborative filtering.

Content-based image retrieval

Content-based image retrieval (CBIR) is a computer vision technique that finds database images similar to a query image using visual features like color, texture, and shape.

Context mixing

Context mixing is a lossless data compression technique that combines the probability estimates of many statistical models, introduced by Matthew Mahoney in PAQ1 in 2002.

Context parallelism

Context parallelism (CP) is a parallelization scheme that splits transformer input sequences and activations across GPUs along the sequence dimension, reducing memory for long-context language model training.

Contextual bandit

A contextual bandit is a sequential decision-making method in machine learning where an algorithm observes context, chooses an action, and learns from only that action's reward, used in recommendation and advertising.

Contextual inquiry

Contextual inquiry is a user research method in human–computer interaction in which a researcher observes and interviews a person while they work in their real work environment.

Continual pre-training

Continual pre-training is the practice of resuming a language model's pre-training on new corpora, such as a new domain, language, or more recent data, instead of training from scratch.

Continual reinforcement learning

Continual reinforcement learning (CRL) is the machine learning setting where an agent learns a sequence of related tasks over time while retaining earlier skills, fighting catastrophic forgetting.

Contourlet transform

The contourlet transform is a multiscale, multidirectional image decomposition built from a Laplacian pyramid and directional filter bank, introduced by Do and Vetterli in 2005 and used in denoising, compression, and fusion.

Contract Net Protocol

The Contract Net Protocol (CNP) is a task-sharing protocol for multi-agent systems, introduced by Reid G. Smith in 1980, in which nodes bid on announced tasks.

Contrastive clustering

Contrastive clustering is an unsupervised deep learning method, introduced by Yunfan Li and colleagues in 2020, that learns representations and cluster assignments jointly by contrasting augmented views of the same instance and cluster.

Contrastive learning

Contrastive learning is a machine learning approach that pulls representations of similar samples together and pushes dissimilar ones apart, without requiring labels, producing encoders that transfer to downstream tasks.

Convection scheme

A convection scheme is the numerical discretization used to approximate the advection term of a transport equation on a discrete grid, balancing accuracy, stability, and boundedness.

Convex optimization

Convex optimization, also called convex programming, is the branch of mathematical optimization that minimizes convex functions over convex sets, so every local minimum is global.

ConvNeXt

ConvNeXt is a family of pure convolutional neural networks for image recognition, introduced in 2022 by Facebook AI Research and UC Berkeley in the paper A ConvNet for the 2020s.

Convolutional neural network

A convolutional neural network (CNN), also called ConvNet, is a neural network that uses convolution to process grid-like data such as images, dominating computer vision in the 2010s.

Cooley–Tukey FFT algorithm

The Cooley–Tukey FFT algorithm is a divide-and-conquer method published by James Cooley and John Tukey in 1965 that computes the discrete Fourier transform in O(N log N) operations instead of O(N²).

Cooperative optimization

Cooperative optimization is a global optimization method that decomposes a hard energy function into coupled subproblems, formulated by Xiaofei Huang in 2004 for computer vision labeling.

Copy-move forgery detection

Copy-move forgery detection (CMFD) is a digital image forensics method that finds regions copied and pasted within the same image to hide or duplicate content.

Copy-on-write

Copy-on-write, also called implicit sharing, is a resource-management technique that shares data cheaply and copies it only when first modified, used in memory, file systems, and snapshots.

CORDIC

CORDIC (COordinate Rotation DIgital Computer), or Volder's algorithm, is a shift-and-add algorithm for trigonometric, hyperbolic, and linear functions, developed by Jack E. Volder in 1959.

GeneralOct 22, 2026

CoRE MOF 2019 database

The CoRE MOF 2019 database is a curated collection of over 14,000 experimental metal–organic framework crystal structures, 12,020 of them porous, prepared for molecular simulation.

Coreference resolution

Coreference resolution is the natural language processing task of grouping all expressions in a text that refer to the same entity, as in "Theresa May" and "she".

Corner detection

Corner detection is a computer vision method that finds image points where intensity changes sharply in multiple directions, used for tracking, matching, and camera calibration.

GeneralOct 26, 2026

Corporate information factory

Corporate Information Factory (CIF), Bill Inmon's top-down data warehouse architecture: a normalized 3NF enterprise data warehouse fed by ETL from operational systems, supplying dependent data marts; contrasted with Kimball's bottom-up dimensional bus.