Activation - In a neural network, an activation is a numerical value produced by a unit or layer for a particular input, commonly after applying an activation function...
Activation function - An activation function transforms a neural network's intermediate values before they pass onward or become outputs.
Activation Layer - An activation layer is a neural-network component that applies an activation function to incoming values.
ADALINE - ADALINE is an early artificial neural model whose name is commonly expanded as adaptive linear neuron or adaptive linear element.
Adaptive neuro fuzzy inference system - An adaptive neuro fuzzy inference system, commonly called ANFIS, combines fuzzy rules with a trainable network structure.
Adaptive resonance theory - Adaptive resonance theory, or ART, is a theory and family of neural models concerned with learning new patterns while preserving useful earlier knowledge.
ALOPEX - ALOPEX is a family of stochastic optimization approaches that guide parameter changes using correlations between those changes and changes in an overall...
Anchoring Bias - Anchoring bias is the tendency for an initial value or reference point to exert disproportionate influence on a later judgment.
Artificial neural network - An artificial neural network is a computational model made from connected units that transform numerical information using adjustable parameters.
Autoregressive Conditional Heteroskedasticity - Autoregressive conditional heteroskedasticity, usually abbreviated ARCH, is a statistical approach for modeling how the variability of a time-series error...
Autoregressive Distributed Lag - An autoregressive distributed lag model, or ARDL model, explains a time-series outcome using its own past values and current or past values of other variables.
Backpropagation - Backpropagation is an efficient method for calculating how a model's loss changes with respect to its parameters.
Backpropagation through structure - Backpropagation through structure is a method for calculating training gradients in recursive neural networks whose computations follow a structured input...
Batch normalization - Batch normalization is a neural-network operation that normalizes selected intermediate values using statistics computed across a training mini-batch, then...
Bayesian neural network - A Bayesian neural network is a neural model that represents uncertainty about its parameters using probability distributions and updates that uncertainty in...
BCPNN - BCPNN stands for Bayesian Confidence Propagation Neural Network.
Biclustering - Biclustering is a way of finding related subsets of rows and columns together in a data matrix.
Bidirectional associative memory - Bidirectional associative memory, or BAM, is a neural associative-memory model that stores relationships between pairs of patterns and can use a cue on...
Boltzmann Machine Network - A Boltzmann machine network is a stochastic neural model that assigns probabilities to configurations through an energy function.
Calibration layer - A calibration layer is an adjustment applied after a model produces predictions, intended to make those predictions better agree with observed outcomes.
Capsule neural network - A capsule neural network organizes groups of neurons into capsules that represent entities and their properties.
Cellular neural network - A cellular neural network is a network of locally connected processing cells whose changing states collectively process information.
CLEVER score - The CLEVER score is a statistical measure intended to estimate a neural classifier's local robustness to adversarial input changes.
Committee machine - A committee machine combines the outputs of several predictive models into one overall response.
Competitive learning - Competitive learning is a learning approach in which processing units compete to respond to an input, and the selected unit or units receive the main...
Compositional pattern-producing network - A compositional pattern-producing network, or CPPN, is a network that combines mathematical functions to generate patterns from coordinates or other...
Concept activation vector - A concept activation vector, or CAV, is a direction in a neural network's internal activation space associated with a concept defined through example inputs.
Confabulation (neural networks) - Confabulation in neural-network discussions can refer to generated patterns that resemble learned material but are not faithful reconstructions of a...
Continual Learning - Continual learning is the study and practice of enabling a model to learn from successive experiences while retaining useful abilities acquired earlier.
Contrastive Hebbian learning - Contrastive Hebbian learning is a neural learning approach that adjusts connections using differences between activity relationships in two network states.
Contrastive Learning - Contrastive learning trains a representation by comparing examples and encouraging relationships that match a defined notion of similarity.
Convolutional deep belief network - A convolutional deep belief network, or CDBN, is a hierarchical probabilistic model that combines deep belief network ideas with convolutional weight sharing.
Convolutional layer - A convolutional layer is a neural-network component that applies learned filters across local regions of an input.
Convolutional neural network - A convolutional neural network, or CNN, is a neural network that uses convolutional layers to learn patterns through local connections and shared filters.
Cover's theorem - Cover's theorem, in pattern recognition, concerns the capacity of linear decision surfaces to separate labeled points and how nonlinear feature...
Deconvolutional Network - A deconvolutional network, in the historical sense used by the supplied ontology, is an unsupervised model that learns hierarchical image representations by...
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 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 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 lambertian networks - Deep Lambertian networks are generative image models that combine learned probabilistic representations with a Lambertian reflectance assumption.
Deep reinforcement learning - Deep reinforcement learning combines reinforcement learning with deep neural networks.
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...
Deliberative layer - A deliberative layer is the part of an agent or robot architecture that performs planning, reasoning, or other computations requiring more time than...
Denoising Auto Encoder - A denoising autoencoder is a neural network trained to reconstruct an original input from a deliberately corrupted version.
Depthwise separable convolutional neural network - A depthwise separable convolutional neural network is a convolutional network that uses depthwise separable convolutions as important building blocks.