Artificial intelligence and data

Deep representation learning

Deep representation learning trains neural networks to produce useful embeddings without labels, using self-supervised objectives grouped into predictive, contrastive, and generative families such as SimCLR and masked autoencoders.

Deep unfolding network

A deep unfolding network, also called an unrolled network, is a neural network built by unrolling an optimization algorithm's iterations into layers with learnable parameters, used mainly for inverse problems like MRI reconstruction.

DeepChem

DeepChem is an open-source Python library for applying machine learning to scientific data, with its strongest coverage in drug discovery, quantum chemistry, and materials science.

DeepDream

DeepDream, also known as Inceptionism, is a computer vision technique and open-source program created at Google in 2015 that amplifies patterns in images, producing hallucinated, dreamlike pictures that became an art phenomenon.

Deepfake

A deepfake is a video, photo, or audio recording that seems real but is created or manipulated with artificial intelligence, typically to replace faces or synthesize speech.

Deepfake detection

Deepfake detection is the machine learning task of deciding whether an image, video, or audio recording is synthetically generated or manipulated. It emerged as a research field after fabricated celebrity videos spread in 2017.

Deepfake pornography

Deepfake pornography, also known as deepfake porn or AI-generated pornography, is sexually explicit imagery of real, identifiable people created with artificial intelligence without consent, first appearing on Reddit in 2017.

DeepH

DeepH is a machine-learning method that uses deep neural networks to predict the density functional theory Hamiltonian of a material from its atomic structure, bypassing costly DFT iterations.

DeepH-E3

DeepH-E3 is an E(3)-equivariant deep-learning framework that predicts a material's DFT Hamiltonian from its atomic structure, developed at Tsinghua University and published in Nature Communications in 2023.

DeepLab

DeepLab is a family of semantic image segmentation methods using deep convolutional neural networks with atrous convolutions, developed in four versions from 2015 to 2018.

DeepONet

DeepONet (deep operator network) is a neural network architecture, proposed in 2019 by Lu Lu, Pengzhan Jin, and George Em Karniadakis, that learns nonlinear operators mapping PDE inputs to solution fields.

DeepPavlov

DeepPavlov is an open-source conversational AI and natural language processing library introduced at ACL 2018 by a team from MIPT, originally for building end-to-end dialog systems and chatbots.

DeepSpeed

DeepSpeed is an open-source deep learning optimization library developed by Microsoft, released in February 2020 and best known for the ZeRO memory optimizer for large models.

DeepTICA

DeepTICA is a machine learning method that builds collective variables for molecular dynamics simulations by combining neural networks with time-lagged independent component analysis, introduced in 2021.

Deequ

Deequ is an AWS Labs open-source Scala library for defining "unit tests for data" on Apache Spark, scaling constraint verification, profiling, incremental metrics, and anomaly detection to billions of rows; PyDeequ provides a Python interface, and AWS Glue and EMR Data Quality run on its engine.

Default logic

Default logic is a formal system for nonmonotonic reasoning introduced by Raymond Reiter in 1980, in which plausible conclusions from incomplete information can be retracted when new facts arrive.

Deformable convolution

Deformable convolution is a neural network operator whose kernel sampling locations shift by learned offsets, letting the grid adapt to object shapes. Introduced at ICCV 2017.

Deformable model (computer vision)

A deformable model in computer vision is a curve or surface pulled by image forces onto object boundaries, best known as the snake, or active contour.

Defuzzification

Defuzzification is the final step of a fuzzy inference system, converting a fuzzy output set into one crisp number that an actuator or decision procedure can use.

Delta encoding

Delta encoding is a data compression technique that stores differences between successive values or versions of data, underpinning revision control, software patching, and storage systems.

Delta Lake

Delta Lake, born as Project Tahoe at Databricks in 2017, is an open-source ACID table format over Parquet files in data lakes, using a JSON transaction log (delta_log), MVCC, time travel, and connectors for Spark, Flink, and Trino.

Delta modulation

Delta modulation is an analog-to-digital encoding method that represents a waveform with one bit per sample, sending whether the signal rose or fell; it underlies sigma-delta converters.

Delta rule

The delta rule, also called the Widrow–Hoff rule or LMS rule, is a supervised learning rule for neural networks developed by Bernard Widrow and Marcian Hoff in 1960.

Delta Sharing

Delta Sharing is an open REST protocol launched by Databricks in 2021 under the Linux Foundation for secure zero-copy sharing of live Delta Lake and Parquet datasets across platforms and clouds; it evolved into OpenSharing in 2026.

Demosaicing

Demosaicing is the image-processing step that reconstructs a full-color RGB image from a camera sensor whose pixels each record only one color, usually in the Bayer pattern.

Denodo

Denodo Technologies, founded 1999 by Ángel Viña in A Coruña, Spain, and headquartered in Palo Alto, is a private data management vendor whose Denodo Platform delivers data virtualization and logical data management, integrating 200+ sources without replication; TPG Growth invested $336 million in 2023.