3,135 articles
DNA computing
DNA computing is a computation paradigm using DNA molecules and biochemical reactions to encode data and compute in massive parallelism, introduced by Leonard Adleman in 1994.
DNA Data Bank of Japan
The DNA Data Bank of Japan (DDBJ) is the Japanese public archive of nucleotide sequence data, operating since 1987 at the National Institute of Genetics in Mishima.
DNA Doe Project
The DNA Doe Project is an American nonprofit founded in 2017 that uses investigative genetic genealogy to identify John and Jane Does, unidentified human remains, for law enforcement agencies.
DNA neural network
A DNA neural network is an artificial neural network implemented with DNA molecules in a test tube, first reported by Caltech researchers in Nature in July 2011, recalling patterns from incomplete inputs.
Doc2vec
Doc2vec, originally published as Paragraph Vector, is an unsupervised algorithm proposed by Quoc Le and Tomas Mikolov in 2014 that learns fixed-length dense vector representations of entire documents.
Docling
Docling: IBM Research's MIT-licensed open-source document conversion and layout analysis engine (Linux Foundation LF AI & Data) that turns PDFs, Office files, HTML, images, and audio into structured Markdown and JSON using DocLayNet layout and TableFormer table models for RAG pipelines.
Document classification
Document classification, also known as document categorization or text categorization, is the task of automatically assigning documents such as web pages and newswire stories to predefined classes by subject, generally using machine learning algorithms.
Document clustering
Document clustering is an unsupervised machine learning method that groups text documents by similarity, typically using TF-IDF vectors and k-means, to support search, topic extraction, and web mining.
Document layout analysis
Document layout analysis is a document image processing step that detects and classifies page regions such as text blocks, tables, and figures to support OCR.
Document-level relation extraction
Document-level relation extraction (DocRE) is a natural language processing method that identifies relations between entity pairs across an entire document, used to build knowledge bases from large-scale documents.
Document-oriented database
A document-oriented database, also called a document store or document database, is a NoSQL database that stores data as self-contained JSON documents, allowing each document its own flexible structure.
Dolt (database)
Dolt is an open-source, MySQL-compatible SQL database with Git-style version control (branch, merge, diff, clone) built on content-addressed Prolly Trees; launched 2019 by DoltHub, with Doltgres for Postgres and Dolt 2.0 in 2026.
Domain adaptation
Domain adaptation is a machine learning setting in which a model trained on labeled data from one distribution is adapted to predict accurately on a different but related distribution.
Domain adaptation network
A domain adaptation network is a neural network trained on labeled source and unlabeled target data so its features transfer across the domain shift, enabling target-domain classification.
Domain-adaptive object detection
Domain-adaptive object detection adapts a detector trained on labeled source images to a target domain whose data distribution differs, using unlabeled target images, mainly through adversarial feature alignment and self-training with pseudo-labels.
Domain-adversarial neural network
A domain-adversarial neural network (DANN) is a neural network for unsupervised domain adaptation, introduced by Yaroslav Ganin and Victor Lempitsky in 2014, that uses a gradient reversal layer to make features indistinguishable across domains.
Domain-adversarial training of neural networks
Domain-adversarial training of neural networks (DANN) is an unsupervised domain adaptation technique introduced by Ganin and Lempitsky in 2014, which uses a gradient reversal layer to learn domain-invariant features.
Domo (company)
Domo, Inc. is an American cloud-based business intelligence and data analytics company founded in 2010 by Omniture founder Josh James and headquartered in American Fork, Utah.
DOT (graph description language)
DOT is a plain-text declarative language for describing graphs in which files state nodes, edges, and attributes, and the Graphviz package renders them to SVG, PostScript, and other formats.
Double deep Q-learning
Double deep Q-learning (Double DQN) is a reinforcement learning algorithm that reduces overestimation of action values by selecting the greedy action with the online network but evaluating it with the target network.
Double deep Q-network
The double deep Q-network, or double DQN, is a reinforcement learning algorithm that reduces overestimation of action values in tasks such as playing Atari games.
Double descent (machine learning)
Double descent is a machine learning phenomenon in which test error follows the classical U-shaped curve, peaks at the interpolation threshold, then falls again with overparameterization.
Double machine learning
Double machine learning, also known as debiased machine learning, is a statistical method for estimating treatment effects using machine learning, introduced by Victor Chernozhukov and colleagues in 2018.
Double-ended queue
A double-ended queue, abbreviated deque, is a data structure in which elements can be added, removed, and examined at both the front and the back.
Doubly linked list
A doubly linked list is a linked data structure whose nodes hold pointers to both the next and previous elements, allowing backward traversal and constant-time deletion.
DPA-2
DPA-2 (deep potential attention-2) is a large atomic model for molecular and materials simulation, introduced in December 2023 and pre-trained across diverse systems for fine-tuning and distillation.
DPLL(T)
DPLL(T) is a satisfiability modulo theories architecture that combines a DPLL SAT solver with specialized theory solvers to decide first-order formulas in theories like equality and arithmetic, introduced at CAV 2004.
Dragonfly algorithm
The Dragonfly algorithm is a swarm intelligence metaheuristic that solves single-objective, discrete, and multi-objective optimization problems by mimicking the static and dynamic swarming behavior of dragonflies.
Drift detection (machine learning)
Drift detection is a family of machine learning monitoring methods that watch incoming data or a deployed model's behavior for distributional change and signal when retraining is needed.
Dropout (neural networks)
Dropout is a regularization technique for training neural networks that randomly removes units and their connections at each step to reduce overfitting, introduced by Geoffrey Hinton and colleagues in 2012.