Mobile cloud computing - Mobile cloud computing combines mobile devices with cloud services to deliver applications, store information or perform processing.
Mobile positioning data - Mobile positioning data describes where a mobile device was estimated to be at particular times.
Modal logic - Modal logic is a family of formal systems that extends ordinary logical statements with ways to express qualifications such as necessity, possibility...
Modal operators - Modal operators are logical symbols that qualify a statement with a meaning such as “necessarily,” “possibly,” “knows,” or “believes.
Modal verb - A modal verb is a verb, commonly an auxiliary in English, that expresses meanings such as possibility, necessity, ability, permission, or obligation...
Modality - A modality is a channel or type of data: text, images, audio, video, tables of numbers, sensor readings.
Mode - The mode is a value or category with the greatest frequency in a dataset, or greatest probability in a discrete distribution.
Mode collapse - Mode collapse is a failure of a generative model to represent enough of the variety in its target distribution.
Mode Confusion Bias - Mode confusion bias occurs when a person misunderstands a system's active operating mode and takes an action that would be appropriate in a different mode.
Mode connectivity - Mode connectivity describes the existence of a continuous path between trained model parameter settings along which a specified loss remains low.
Model - A model is a structured representation used to describe relationships, generate predictions, simulate behavior, or support reasoning about a system.
Model-agnostic meta-learning - Model-agnostic meta-learning, usually abbreviated MAML, learns initial model parameters that can adapt effectively to new tasks through a small number of...
Model assertion - A model assertion is a check over the inputs and outputs of one or more machine-learning models that flags behavior inconsistent with an expected property.
Model-based agent - A model-based agent is an agent that keeps an internal model of how its world works, so it can act sensibly even when its current view is incomplete.
Model-based clustering - Model-based clustering groups observations by fitting an explicit statistical model for how the data could have been generated.
Model based imputation - Model based imputation replaces missing values using an estimated or specified relationship in the data.
Model-based reasoning - Model-based reasoning draws conclusions by using an explicit representation of how a system is structured or behaves.
Model-based reinforcement learning - Model-based reinforcement learning uses a model of an environment's consequences to help an agent learn or choose actions that maximize expected cumulative...
Model capacity - Model capacity is the range or complexity of relationships that a model class can represent and potentially learn.
Model card - A model card is structured documentation that accompanies a machine-learning model and explains its intended uses, evaluation evidence, relevant...
Model cascading - Model cascading is an inference-system strategy that directs requests among models with different capabilities and resource costs, often allowing a smaller...
Model-Centric ML - Model-centric machine learning is an approach that emphasizes improving a model's architecture, learning algorithm or hyperparameters while working with a...
Model checking - Model checking is a method of determining whether a formally specified property holds in the interpretations or system behaviors represented by a model.
Model debugging - Model debugging is the systematic investigation and correction of undesirable behavior in a machine-learning model and the data, code and processes that...
Model decay - Model decay is a deterioration in a deployed machine-learning model's useful performance over time.
Model-Dependent Transformations - Model-dependent transformations are feature-processing steps configured for a particular model and applied consistently when that model is trained and used...
Model Deployment - Model deployment is the process of making a trained model available in an operating environment where an application can use it for inference.
Model editing - Model editing is the targeted modification of a trained model's behavior or represented information without repeating its complete original training process.
Model extraction - Model extraction is the recovery or approximation of a machine-learning model's functionality, structure or parameters through access to the model or its...
Model-free (reinforcement learning) - Model-free reinforcement learning learns value estimates or a policy from experience without using an explicit model of the environment's transition and...
Model Governance - Model governance is the set of responsibilities, policies and decision processes used to oversee models throughout their lifecycle.
Model-Independent Transformations - Model-independent transformations are data-processing steps that produce features whose definitions are not tied to one particular downstream model.
Model Inference - Model inference is the use of a trained machine-learning model to produce outputs from input data.
Model Interpretability - Model interpretability is the extent to which people can understand relevant aspects of a model's behavior and the meaning of its outputs.
Model inventory - A model inventory is an organized, maintained record of the models an organization develops, obtains or uses, together with information needed to understand...
Model inversion - Model inversion is a privacy-related inference process that uses access to a trained model, its outputs or related information to infer sensitive attributes...
Model order reduction - Model order reduction constructs a lower-dimensional model that approximates important behavior of a larger dynamical system.
Model overlay - A model overlay is a documented adjustment applied to a model's inputs, parameters or outputs to address a limitation, missing risk or circumstance that the...
Model parallelism - Model parallelism distributes parts of a model's computation across multiple processing devices.
Model parameter - A model parameter is a quantity that helps specify a particular model within a family of possible models.
Model poisoning - Model poisoning is a deliberate attack that corrupts a model through control over its parameters, updates or other model-level components.
Model preference - Model preference is a way of ranking or partially ordering the interpretations that satisfy a logical knowledge base so that conclusions can be drawn from...
Model Quantization - Model quantization represents a model's numerical values using a more limited set of values, often with fewer bits than the original representation.
Model Registry - A model registry is a system for organizing named machine-learning models, their versions, and associated metadata so that teams can identify, retrieve, and...
Model risk management - Model risk management is the process of identifying, assessing, reducing and monitoring risks arising from models and their use.
Model router - A model router is a component that chooses which available model should handle a particular input or task.
Model selection - Model selection is the process of choosing a model or modeling configuration from a set of candidates using evidence and criteria suited to a task.
Model Selection Bias - Model selection bias is distortion introduced when a model is chosen using data and the chosen result is interpreted without accounting for that selection.