Fast marching method - The fast marching method is a numerical technique for computing arrival times of a front that advances monotonically through a domain.
Fast multipole method - The fast multipole method, or FMM, is a family of algorithms for rapidly evaluating large sums of interactions between sources and targets.
Fast probability integration - Fast probability integration, or FPI, refers to efficient numerical approaches for evaluating probabilities defined by uncertain model inputs and a...
Fat Relation - Fat relation is an informal term for a relation with many attributes, often described more plainly as a wide table.
Fault tolerance - Fault tolerance is a system's ability to continue providing an acceptable service when one or more components fail.
Favorable label - A favorable label is the outcome category designated as desirable or beneficial for the people considered in a fairness analysis.
FCO-IM - FCO-IM stands for Fully Communication Oriented Information Modeling.
Feature - A feature is one measurable input field a model learns from, such as a customer's age, an order's total or the hour a call arrived.
Feature cross - A feature cross is a new input made by combining two or more existing fields, so the model can learn patterns that only appear when the fields interact.
Feature cutoff - A feature cutoff is a threshold that drops feature values below a set frequency or importance, keeping the input space small enough to be useful.
Feature descriptor - A feature descriptor is a numerical representation of selected characteristics of an image, image region or detected feature.
Feature engineering - Feature engineering is the work of choosing, transforming and inventing the input fields a model learns from, turning raw data into information the model...
Feature extraction - Feature extraction is deriving informative measurements from raw, unstructured material, such as turning a photo into edges and textures or a document into...
Feature extractor - A feature extractor is the component that converts raw input into feature values: the fixed recipe, pipeline stage or trained network that stands between...
Feature Freshness - Feature freshness is how up to date a model's input values are: the gap between what the data says and what is true right now.
Feature Function - A feature function is the reusable piece of logic that computes one feature from raw data: given the inputs, here is exactly how this field gets its value.
Feature hashing - Feature hashing is a trick for turning huge or open-ended sets of values into a fixed number of numeric columns, by passing each value through a hash...
Feature interactions - Feature interactions are the ways two or more input fields affect the outcome jointly, so that the effect of one depends on the value of another.
Feature learning - Feature learning is letting a model discover the useful input representations itself from raw data, instead of a person designing the features by hand.
Feature map - A feature map is the output produced when a learned detector is applied across an input, such as the grid of responses an edge detector leaves at every...
Feature matrix - A feature matrix is the table a model trains on: one row per example, one column per feature, with each cell holding that example's value for that field.
Feature Monitoring - Feature monitoring is watching a live model's input values over time, so you catch the moment the data stops looking like what the model was trained on.
Feature Pipeline - A feature pipeline is the automated path that turns raw data into model-ready features on a schedule: extraction, transformation, validation and delivery...
Feature Selection - Feature selection is choosing which of the available input fields a model should actually use, keeping the ones that carry signal and dropping the rest.
Feature set - A feature set is the chosen collection of input fields a model uses: the full list of what the model may consider, selected, named and versioned as one unit.
Feature shift - Feature shift is the change in an input field's values or distribution between when a model was trained and when it is used, a specific form of drift...
Feature template - A feature template is a reusable pattern for generating a family of similar features: define the pattern once, then stamp out a feature for every value...
Feature vector - A feature vector is one record's values across all the model's input fields, written as an ordered list of numbers: that customer, order or event, as the...
Federated averaging - Federated averaging, often shortened to FedAvg, is a federated-learning algorithm that combines locally trained model parameters into a shared model.
Federated learning - Federated learning is training a shared model across many devices or organizations without moving their raw data to one place: the training goes to the...
Federated learning network - A federated learning network is a representation of the participants and collaboration relationships in a federated learning application.
Federated learning system - A federated learning system is the complete arrangement of participants, local data, computing resources, communication and software used to train models...
Federated stochastic gradient descent - Federated stochastic gradient descent, or FedSGD, is a federated training method that uses stochastic or approximate local gradients rather than requiring...
Federation (information technology) - Federation in information technology is an arrangement in which separately managed systems cooperate through agreed interfaces and rules while retaining a...
Feed forward chunking - Feed forward chunking is a memory-management technique that processes a transformer's position-wise feed-forward computation in smaller groups of token...
Feed forward (control) - Feedforward control determines a control action from a reference signal, a measured disturbance, or other predictive information, without relying on the...
Feed-forward network - A feed-forward network is a neural network whose prediction computation moves through a directed structure without recurrent cycles.
Feed-forward neural network density model - A feed-forward neural network density model uses a standard layered network to estimate the probability distribution of the data itself, so it can score how...
Feed-forward neural network regression - Feed-forward neural network regression is using a standard layered network to predict a number, such as a price, a demand or a risk score, from input features.
Feed-forward stylization - Feed-forward stylization is the generation of a stylized image by passing an input through a trained model, without running a separate image-optimization...
Feedback - Feedback is information about how things went, returned to the system that produced them, so future behavior can adjust: outcomes flowing back to the actor.
Feedback loop - A feedback loop is the closed circuit in which a system's outputs affect its future inputs: what the system does changes the world it then reads, for better...
Feedback Loop Bias - Feedback Loop Bias occurs when a system's outputs influence the behavior or observations that later become its learning data, reinforcing a distorted pattern.
Feedback Network - In neural-network terminology, a feedback network contains connections through which information can return to an earlier part of the computation, creating...
Feedback neural network - A feedback neural network includes connections that allow earlier internal activity or later-layer information to influence subsequent processing.
Feedforward neural network - A feedforward neural network is a neural network whose prediction computation follows directed connections without cycles.