Elementary matrix - An elementary matrix is a square matrix obtained by applying one elementary row operation to an identity matrix.
Elias delta coding - Elias delta coding is a self-delimiting binary code for positive integers.
Elias gamma coding - Elias gamma coding is a self-delimiting binary representation of positive integers.
Elias omega coding - Elias omega coding is a self-delimiting binary code for positive integers that recursively represents the lengths needed to describe a number.
Eligibility trace - An eligibility trace is a temporary memory used by a learning algorithm to determine which recently active states, actions or features should be updated...
Elitism - Elitism is an evolutionary-search mechanism that preserves one or more of the best current candidate solutions when forming the next population.
Elliptical distribution - An elliptical distribution is a multivariate probability distribution obtained by shifting and linearly transforming a spherically symmetric random vector.
Elliptical weighted average (EWA) - Elliptical weighted average, or EWA, filtering estimates a resampled image or texture value by combining samples within an elliptical footprint using...
Elman Networks - Elman networks are simple recurrent neural networks that feed the previous hidden state into the computation of the next hidden state.
ELMo - ELMo, short for Embeddings from Language Models, is a method for producing word representations that change with the surrounding text.
ELU Function - The ELU function, short for exponential linear unit, is a neural-network activation function that is linear for positive inputs and follows a saturating...
Embarrassingly parallel - An embarrassingly parallel workload can be divided into tasks that run independently with little or no communication between them during computation.
Embedded analytics - Embedded analytics is the integration of analytical information and interactions into another application, portal or website.
Embedded SQL - Embedded SQL is SQL written within a program in a host language, such as C or COBOL, using a supported embedding syntax and processing system.
Embedded zerotrees of wavelet transforms - Embedded zerotrees of wavelet transforms refers to the embedded zerotree wavelet, or EZW, approach to image compression introduced by Jerome Shapiro.
Embedding - An embedding is a numerical representation that places an item in a vector space where relationships can be useful for a machine-learning task.
Embedding layer - An embedding layer maps discrete identifiers, such as token or category IDs, to dense numerical vectors.
Embedding lookup layer - An embedding lookup layer retrieves numerical vectors from an embedding table using discrete input identifiers.
Embedding matrix - An embedding matrix is a table of numerical vectors, typically with one row for each token or category and one column for each embedding dimension.
Embedding space - An embedding space is the vector space in which a model represents items as numerical coordinates.
Embedding vector - An embedding vector is an ordered list of numerical coordinates representing an item for a machine-learning task.
Embodied agent - An embodied agent perceives and acts through a body situated in an environment.
Emergent algorithm - An emergent algorithm produces a larger pattern of behavior through interactions among simpler components following local rules.
Emergent Bias - Emergent bias is systematic unfairness that becomes apparent through the interaction of a computer system with its real context of use, particularly as...
Emission probabilities - Emission probabilities describe how likely particular observations are given a hidden state in a probabilistic model such as a hidden Markov model, or HMM.
Emphatic temporal-difference learning - Emphatic temporal-difference learning is a family of value-prediction methods that changes the emphasis placed on updates to address instability in...
Empirical Bayes - Empirical Bayes is an approach that estimates aspects of a Bayesian analysis, commonly the prior distribution or its hyperparameters, from observed data.
Empirical cumulative distribution function - The empirical cumulative distribution function, or ECDF, gives the proportion of observed sample values that are less than or equal to a specified threshold.
Empirical distribution - An empirical distribution is the probability distribution that assigns equal mass to each observation in a nonempty, unweighted dataset.
Empirical dynamic modeling - Empirical dynamic modeling, or EDM, is a family of methods for studying and predicting changing systems by reconstructing their dynamics from observed...
Empirical gradient - In the local-search terminology used by Artificial Intelligence: A Modern Approach, an empirical gradient is an estimate of how an objective changes...
Empirical loss - Empirical loss is the loss measured on a finite set of observed examples, usually calculated as the average of a per-example loss function.
Empirical risk - Empirical risk is a model's average loss on a specified collection of observed examples.
Empirical risk minimization - Empirical risk minimization, or ERM, is the learning principle of choosing a model from an allowed set to minimize average loss on the training examples.
Empowerment (artificial intelligence) - In AI, empowerment is an information-theoretic measure of how much an agent's actions can influence its future observations.
Emulation - Emulation is the reproduction of relevant behavior of one system by another system so that software, users, or connected components can interact with the...
Enactive interfaces - Enactive interfaces are human - computer interfaces designed around the continuing relationship between bodily action and perception.
Encoder - An encoder is the part of a model that reads an input and compresses it into an internal numeric form that captures what matters about it for the task.
Encoder-decoder - An encoder-decoder is a model architecture in which an encoder represents an input and a decoder uses that representation to produce an output.
Encoder models - Encoder models transform an input into learned internal representations that can support prediction, matching, or extraction.
Encoding (for Features) - Encoding, for features, is converting a field into a numeric form a model can use, such as turning the category red, amber or green into numbers without...
End-to-end learning - End-to-end learning trains a system's learnable processing stages together using an objective tied to its final output.
End-to-end training - End-to-end training uses a downstream task objective to adjust parameters across a connected model, including earlier representation-building components.
Endpointing - Endpointing is deciding when a spoken utterance or input segment is complete enough for a speech system to finalize recognition or move to its next action.
Energy-based model - An energy-based model assigns a numerical energy score to a possible configuration or an input-output combination.
Energy distance - Energy distance is a measure of difference between probability distributions built from expected distances between independent observations.
Energy in frame - Energy in a frame is a measure of signal strength over a short segment of audio.