3,135 articles
ExecuTorch
ExecuTorch is an open-source stack from Meta for running PyTorch machine learning models directly on edge devices, from phones to microcontrollers, without a cloud connection.
Expected improvement
Expected improvement (EI) is an acquisition function in Bayesian optimization that picks the next evaluation where the expected gain over the best value so far is largest.
Expert parallelism
Expert parallelism is a parallelism strategy for mixture-of-experts models that places each expert's feedforward layer on a different accelerator, scaling models without extra compute per token.
Expert system
An expert system is a computer program that emulates a human specialist's decision-making in a narrow domain by reasoning over explicitly encoded if–then rules rather than learning from data.
Explainable artificial intelligence
Explainable artificial intelligence (XAI), also called interpretable AI, comprises methods for making machine-learning models understandable, either by design or through post-hoc explanations of black-box systems.
Exponential backoff
Exponential backoff is a retry strategy in which the delay between attempts grows by a constant factor after each failure, usually combined with jitter to prevent synchronized retries.
Exponential search
Exponential search, also called doubling search or galloping search, is a search algorithm for sorted arrays, described by Jon Bentley and Andrew Yao in 1976.
Expressed sequence tag
An expressed sequence tag (EST) is a short single-pass sequence read from a cDNA clone, typically under 1,000 base pairs, that tags genes expressed in the sampled tissue.
EXPSPACE
EXPSPACE (exponential space) is the complexity class of decision problems solvable by a deterministic Turing machine using memory bounded exponentially in input length; equivalence of regular expressions with squaring requires it.
EXPTIME
EXPTIME, also written EXP, is the complexity class of decision problems solvable by a deterministic Turing machine in time 2^(n^k); it strictly contains P, and its complete problems include generalized chess, checkers, and Go.
Extension principle
The extension principle is a rule in fuzzy set theory, introduced by Lotfi Zadeh, that extends ordinary functions and relations to fuzzy sets and underlies fuzzy arithmetic.
External sorting
External sorting, also called external sort, sorts data too large for main memory by moving blocks between memory and storage, typically via external merge sort.
Extract, load, transform
Extract, load, transform (ELT) is a data integration pattern that loads raw data into a cloud warehouse or lake before transforming it there, reversing ETL's order; typically uses SQL tools such as dbt.
Extract, transform, load
Extract, transform, load (ETL) is a data integration process, popular since the 1970s, that extracts data from multiple sources, transforms it, and loads it into a data warehouse.
Extreme learning machine
An extreme learning machine (ELM) is a single-hidden-layer feedforward neural network whose input weights are random and fixed, so training reduces to a fast linear solve for the output weights.
Extremely randomized trees
Extremely randomized trees (Extra-Trees) is an ensemble machine learning method that builds many unpruned decision trees using randomly drawn split points, introduced by Geurts, Ernst, and Wehenkel in 2006.
Extrinsic calibration
Extrinsic calibration is the estimation of a camera or sensor's rigid-body position and orientation relative to a reference frame or second sensor, so measurements share one coordinate system.
Eye detection (computer vision)
Eye detection is a computer vision method that locates human eyes in images or video frames, typically feeding gaze estimation, biometrics, and driver drowsiness monitoring.
Façade (video game)
Façade is a free 2005 interactive drama for Windows by Michael Mateas and Andrew Stern, in which the player types dialogue during a couple's marriage collapse.
Face anti-spoofing
Face anti-spoofing, also called face presentation attack detection, is the computer vision task of deciding whether a camera sees a live person or an artifact like a photo, replayed video, or 3D mask.
Face detection
Face detection is a computer vision method that locates human faces in images or video, outputting a bounding box and confidence score for each face found.
Face liveness detection
Face liveness detection, also called face anti-spoofing, is a computer vision method that decides whether a face shown to a camera is a live person or a spoof.
Facebook–Cambridge Analytica data scandal
The Facebook–Cambridge Analytica data scandal, also called the Cambridge Analytica scandal, was the 2018 revelation that a personality-quiz app harvested profile data on up to 87 million Facebook users.
facial age estimation
Facial age estimation is the inference of a person's age or age range from a face photograph using computer vision models; the best systems average about 2.4 years of error, with the 16-to-18 boundary hardest.
Facial expression recognition
Facial expression recognition is a computer vision task that classifies emotional expressions from images or video of faces, contested scientifically and restricted by the EU AI Act.
Facial landmark detection
Facial landmark detection is a computer vision method that automatically locates predefined points on a human face in an image, such as eye corners, nose tip, and mouth edges.
Facial recognition system
A facial recognition system, also known as face recognition, is a biometric technology that identifies or verifies people by automated comparison of facial features extracted from images.
Fact constellation
Fact constellation (galaxy schema) is a dimensional data-warehouse design with multiple fact tables, such as sales and shipping, connected only through shared, conformed dimension tables enabling multi-fact analysis.
Fact table
Fact table: the central table of a Kimball star schema, storing numeric measures of business events keyed by dimension foreign keys. Types: transaction, periodic snapshot, accumulating snapshot; measures additive, semi-additive, or non-additive.
Factor graph optimization
Factor graph optimization is a probabilistic inference method that estimates unknown variables by minimizing a cost over a factor graph, dominant in robotics and SLAM.