Decision theory - Decision theory studies how choices relate to beliefs, uncertainty and preferences.
Decision tree - A decision tree is a model that predicts an answer through a sequence of yes-or-no style questions about the input, arranged like the branches of a tree...
Decision tree pruning - Decision tree pruning is the practice of cutting branches off a grown decision tree so it stops memorizing the training data's noise and predicts new cases...
Deck imputation - Deck imputation fills a missing data item using information from a donor record.
Decoder - A decoder is the part of a model that turns an internal numeric representation back into an output people can use, such as words, images or predictions.
Decoder models - Decoder models, in the language-modeling sense used here, generate sequences by predicting the next token from preceding context.
Decoder-only model - A decoder-only model is a neural sequence model built around a decoder stack without a separate encoder stack for the text input.
Decoding - Decoding is the inference process that turns a model's scores or probabilities into a selected output sequence.
Decomposition - Decomposition is the process of expressing a problem or structure as smaller parts whose solutions or properties can be combined to address the original whole.
Decomposition Algorithm - A decomposition algorithm is a systematic procedure for dividing a relation schema into smaller relation schemas to satisfy a chosen database normal form.
Decomposition of time series - Decomposition of time series separates observations recorded over time into components representing different patterns.
Deconvolutional Network - A deconvolutional network, in the historical sense used by the supplied ontology, is an unsupervised model that learns hierarchical image representations by...
Deduction theorem - The deduction theorem connects reasoning from an assumption with reasoning about an implication.
Deductive classifier - A deductive classifier is a reasoning system that determines classifications from formally stated knowledge and logical consequences.
Deductive databases - Deductive databases combine stored facts with logical rules that derive additional information when answering queries.
Deductive imputation - Deductive imputation supplies a missing value by applying logical relationships to information already available.
Deductive language - A deductive language is a language for expressing facts, relationships and logical rules so that a system can derive conclusions from them.
Deductive learning - Deductive learning is learning that uses existing knowledge and logical reasoning to derive reusable knowledge or procedures.
Deductive synthesis - Deductive synthesis is the construction of a program from a specification by applying logical reasoning and justified transformation rules.
Deep Active Learning - Deep active learning combines deep neural models with a process for selecting which examples should receive labels next.
Deep Belief Network - A deep belief network, or DBN, is a generative probabilistic model with multiple layers of hidden variables.
Deep Convolutional Inverse Graphics Network - A Deep Convolutional Inverse Graphics Network, commonly abbreviated DC-IGN, is a particular generative image model designed to learn a representation that...
Deep Copy - A deep copy creates a new compound object and recursively copies its contained objects according to the language or library's copying rules.
Deep deterministic policy gradient - Deep deterministic policy gradient, or DDPG, is an off-policy actor-critic reinforcement-learning algorithm for continuous action spaces.
Deep ensemble - A deep ensemble combines predictions from several trained deep neural networks.
Deep Equality - Deep equality compares the contents of structured values beyond their outermost identity, following nested components according to a defined comparison rule.
Deep Feed-Forward Network - A deep feed-forward network is a neural network that transforms inputs into outputs through multiple successive processing layers without recurrent feedback...
Deep Gaussian process - A deep Gaussian process is a probabilistic model that composes multiple mappings governed by Gaussian processes.
Deep image prior - Deep image prior is an image-restoration approach that uses the structure of a randomly initialized neural network as a bias towards certain kinds of images.
Deep kernel learning - Deep kernel learning combines a learned deep neural representation with a kernel method.
Deep lambertian networks - Deep Lambertian networks are generative image models that combine learned probabilistic representations with a Lambertian reflectance assumption.
Deep learning - Deep learning is a branch of machine learning that learns representations through multiple layers of computation.
Deep learning speech synthesis - Definition: Deep learning speech synthesis uses neural networks with multiple processing layers to generate speech audio.
Deep linguistic processing - Definition: Deep linguistic processing analyses language using detailed grammatical and semantic structure.
Deep Markov model - A deep Markov model is a probabilistic sequence model with latent states whose transition or observation distributions are parameterized by neural networks.
Deep model - A deep model is a neural network that transforms inputs through multiple hidden layers before producing an output.
Deep Q-Network - A Deep Q-Network is a reinforcement learning system that uses a neural network to estimate the value of each available action in a given situation, so the...
Deep reinforcement learning - Deep reinforcement learning combines reinforcement learning with deep neural networks.
Deep tomographic reconstruction - Deep tomographic reconstruction uses deep-learning methods to recover an internal image or volume from indirect measurements.
Deep Transfer Learning - Deep transfer learning reuses knowledge learned by a deep neural network in one setting to help solve a different, related task or work with a different...
Deepfake - A deepfake is synthetic or manipulated media created with deep-learning techniques that can make a person or event appear to be something it is not.
Default logic - Default logic is a formal approach to reasoning with assumptions that may be withdrawn when additional information becomes available.
Default rules - Default rules are rules that permit a provisional conclusion when stated conditions hold and no relevant defeating information prevents the inference.
Defective matrix - A defective matrix is a square matrix that lacks enough linearly independent eigenvectors to form a basis.
Defective sample - A defective sample, in the historical statistical terminology used by the supplied glossary, is a sample made incomplete because the intended inquiry was...