Residual Neural Network - A residual neural network is a neural network built with blocks that add a learned transformation to a shortcut transformation of the block's input.
Restricted Boltzmann Machine - A restricted Boltzmann machine, or RBM, is an energy-based probabilistic model with visible units representing observed data and hidden units representing...
RNN-T - RNN-T stands for recurrent neural network transducer.
SELU Function - The SELU function, short for scaled exponential linear unit, is a neural-network activation function with a scaled linear positive branch and a saturating...
Sensor interface layer - A sensor interface layer is the part of a robotic or other sensing system that receives measurements from sensors and presents them to downstream software...
Sigmoid - A sigmoid is an S-shaped mathematical function. In machine learning, the term usually refers to the logistic sigmoid, sigma(z) = 1/(1 + exp(minus z)).
Sigmoid layer - A sigmoid layer applies the logistic sigmoid function to each element of an input tensor, producing outputs between zero and one for finite inputs in exact...
Sigmoid perceptron - A sigmoid perceptron is a neural unit that forms a weighted sum of its inputs, adds a bias, and applies a sigmoid activation to produce a smooth output.
Softplus Function - The Softplus function is a smooth activation function defined by softplus(x) = ln(1 + exp(x)).
Softsign Function - The Softsign function is a neural-network activation defined by softsign(x) = x/(1 + |x|), where |x| means the absolute value of x.
Sparse Auto Encoder - A sparse autoencoder is a neural network trained to reconstruct its input while encouraging a sparse internal representation.
Sparse Learning - Sparse learning is a family of machine-learning approaches that favor models or representations in which relatively few components are nonzero or active.
Stacked RNNs - Stacked RNNs are recurrent neural networks arranged in multiple recurrent layers, with the output sequence from one layer feeding the next.
Stochastic parrot - “Stochastic parrot” is a critical metaphor for a language model that produces convincing language from learned statistical patterns without the grounded...
Supervised Biclustering - Supervised biclustering identifies related groups of rows and columns in a data matrix while using labels, outcomes or other supervising information to...
Swish Function - The Swish function is a neural-network activation that multiplies an input by a sigmoid gate. Its parameterized form is Swish(x) = x × sigmoid(beta × x).
Symmetrically Connected Network - A symmetrically connected network is a neural network in which the connection weight from unit i to unit j equals the weight from j to i.
Synaptic weight - A synaptic weight describes the strength of influence that one neuron or computational unit has on another.
t-Distributed Stochastic Neighbor embedding - t-Distributed Stochastic Neighbor Embedding, usually written t-SNE, is a nonlinear method for representing high-dimensional data in a low-dimensional map...
Tanh - Tanh, short for hyperbolic tangent, is a mathematical function commonly used as a neural-network activation.
Tensor product network - A tensor product network is a connectionist model that uses tensor products to represent bindings between roles and their fillers.
Text-to-image personalization - Text-to-image personalization adapts image generation to a particular subject or concept, such as a specific toy, product or visual style.
Three-layer architecture - In robotics, a three-layer architecture organizes control into reactive control, sequencing and deliberation.
Threshold Autoregressive - A threshold autoregressive, or TAR, model is a time-series model whose autoregressive rule changes according to a threshold variable.
Time-aware long short-term memory - Time-aware long short-term memory is an LSTM-based approach that explicitly uses elapsed time between observations when updating its memory.
Topological deep learning - Topological deep learning develops neural models that use structured relationships described by topological or higher-order mathematical objects.
U-matrix - A U-matrix, or unified distance matrix, is a visualization of distances between neighboring prototype vectors in a trained self-organizing map.
Universal approximation theorem - The universal approximation theorem refers to results showing that suitable neural-network families can approximate broad classes of functions as closely as...
Unsupervised Biclustering - Unsupervised biclustering discovers related subsets of rows and columns in a data matrix without using target class labels to define those groups.
Vector Autoregression - Vector autoregression, or VAR, is a statistical time-series model that predicts several variables together using their own past values and the past values...
Vehicle interface layer - A vehicle interface layer is the software boundary that translates between a vehicle's hardware-specific messages and the more general commands and status...
Vocabulary Reduction - Vocabulary reduction is a set of techniques for decreasing the number of distinct text units a language system represents or considers during a computation.
Winner-take-all (computing) - Winner-take-all, or WTA, is a computational selection mechanism in which one candidate remains active while competing candidates are suppressed or excluded.
word2vec-CBOW - word2vec-CBOW is the continuous bag-of-words architecture for learning word vectors by predicting a target word from nearby context words.
You Only Look Once - You Only Look Once, usually abbreviated YOLO, is the name of an influential object-detection approach and a broad family of later detectors.