3,135 articles
Computer graphics
Computer graphics is the field concerned with producing pictures using a computer, spanning the hardware, software, and applications, including film special effects, games, simulation, medical imagery, and design.
Computer vision
Computer vision is the field of computing concerned with visual perception by computers, extracting information from images and video to recognize objects and reconstruct scenes.
Computer vision dazzle
Computer vision dazzle, also called CV Dazzle or anti-surveillance makeup, is bold makeup and hairstyling created by artist Adam Harvey in 2010 to defeat face detection and facial recognition software.
Computing with words
Computing with words (CWW) is a fuzzy-logic methodology, introduced by Lotfi Zadeh in 1996, in which words rather than numbers are computed and reasoned with.
ConceptNet
ConceptNet is a freely available, multilingual commonsense knowledge base connecting words and phrases with everyday relationships, created by Hugo Liu and Push Singh at the MIT Media Lab in 2004 and maintained by Luminoso Technologies.
Conceptual Captions
Conceptual Captions (CC3M) is a Google dataset of about 3.3 million image-caption pairs harvested from the alt text of web images, introduced in 2018 and widely used for vision-and-language pre-training.
Conceptual graph
A conceptual graph (CG) is a graph representation for logic, introduced by John F. Sowa in 1976 to express the meaning of natural-language sentences for computers.
Conceptual model
A conceptual model, also called an abstract model, is a technology-independent representation of a domain's data, made in the initial phase of database design or software development.
Concurrency (computer science)
Concurrency in computer science is the composition of a system from multiple tasks that may be in progress during the same period, underpinning operating systems and databases.
Conditional generative adversarial network
A conditional generative adversarial network (cGAN) is a GAN trained to generate data controlled by an auxiliary input such as a class label, introduced by Mirza and Osindero in 2014.
Conditional random field
A conditional random field (CRF) is a probabilistic graphical model for structured prediction, introduced in 2001, that predicts whole label sequences and avoids the label bias problem.
Conference on Computer Vision and Pattern Recognition
The Conference on Computer Vision and Pattern Recognition (CVPR) is the annual flagship research conference of the computer vision community, sponsored by the IEEE Computer Society and the Computer Vision Foundation.
Conference on Machine Translation
The Conference on Machine Translation (WMT) is an annual machine translation conference, begun in 2006 as a workshop and made standalone in 2016, whose test sets serve as standard benchmarks.
Conference on Neural Information Processing Systems
The Conference on Neural Information Processing Systems, known as NeurIPS and formerly NIPS, is an annual machine learning and computational neuroscience conference founded in 1987.
Confidence calibration
Confidence calibration is the machine learning task of adjusting a classifier's predicted probabilities to match the true probability that its predictions are correct, measured by expected calibration error.
Configuration management database
A configuration management database (CMDB) is a repository that stores an IT environment's configuration items, such as servers and applications, and the relationships between them.
Conflict-driven clause learning
Conflict-driven clause learning (CDCL) is a complete search algorithm for Boolean satisfiability that records a clause explaining each conflict, pruning the search space and enabling backjumping.
Conflict-free replicated data type
A conflict-free replicated data type (CRDT) is a data structure that can be replicated across computers and updated without coordination, with replicas converging to the same state.
Confluent
Confluent, also known as Confluent, Inc., is an American data streaming company founded in 2014 by the engineers who created Apache Kafka, acquired by IBM in 2026.
Conformal calibration (machine learning)
Conformal calibration is a machine learning technique that wraps an already-trained model's outputs in conformal prediction machinery, producing prediction sets or calibrated distributions with distribution-free, finite-sample coverage guarantees under exchangeability.
Conformance checking
Conformance checking is a process mining technique that compares an event log recorded from a real process against a process model, quantifying deviations with metrics such as fitness.
Conformed dimension
Conformed dimension: Kimball data warehouse concept; a dimension table shared identically (same keys, column names, attribute values) across multiple fact tables, enabling drill-across reports and the bus matrix.
Conformer (machine learning model)
The Conformer is a neural network architecture for automatic speech recognition that combines Transformer self-attention with convolutional modules, proposed by Google researchers in 2020 and presented at Interspeech.
Connectionist temporal classification
Connectionist temporal classification (CTC) is an objective function, introduced by Alex Graves and colleagues in 2006, for training neural networks to label unsegmented input sequences directly, first used widely in speech recognition.
Consensus (computer science)
Consensus, or distributed consensus, is the problem of getting processes in a distributed system to agree on a value even when some crash or messages are lost.
Consensus-based optimization
Consensus-based optimization (CBO) is a derivative-free swarm method for non-convex objectives, introduced by René Pinnau and colleagues in 2017, with provable convergence and applications in machine learning.
Consistent hashing
Consistent hashing, also called stable hashing, is a technique for distributing keys among servers so that few keys move when servers are added or removed, introduced by David Karger and colleagues in 1997.
Constrained clustering
Constrained clustering is a machine learning method that groups data while satisfying user-provided must-link and cannot-link constraints, named by Kiri Wagstaff and Claire Cardie in 2000.
Constrained reinforcement learning
Constrained reinforcement learning trains an agent to maximize reward under explicit limits on cost, risk, or resources, and is standard in safe reinforcement learning for robotics, driving, and healthcare.
Constraint programming
Constraint programming is an approach where a problem is stated by constraints on decision variables, and a solver finds satisfying assignments by interleaving constraint propagation with systematic search.