Leapfrog integrator - The leapfrog integrator is a numerical method that advances position and momentum through alternating half and full steps.
Likelihood tempering - Likelihood tempering changes the influence of observed data by raising the likelihood to a power before combining it with the prior.
Local level model - A local level model represents a time series as noisy observations of an underlying level that changes through a random walk.
Local linear trend model - A local linear trend model is a state-space time-series model with both a changing level and a changing slope.
Log-Gaussian Cox process - A log-Gaussian Cox process is a model for randomly located events in which the logarithm of an underlying event intensity follows a Gaussian process.
Log-sum-exp trick - The log-sum-exp trick is a numerically stable way to calculate the logarithm of a sum of exponentials.
Manifestation shift - Manifestation shift is a change in how a target condition produces the observable physical characteristics used to recognize it.
Masked autoregressive flow - A masked autoregressive flow, or MAF, is a normalizing-flow model that uses masked neural networks to transform observed data into a simpler base distribution.
Matching network - A matching network is a few-shot classification model that predicts a query's label by attending to labeled examples in a support set.
Matrix normal distribution - The matrix normal distribution is a probability distribution for a matrix whose entries jointly follow a multivariate Gaussian distribution with a separable...
Max-product algorithm - The max-product algorithm is a message-passing method for finding a highest-probability joint assignment in a factorized probability model.
Maximum entropy reinforcement learning - Maximum entropy reinforcement learning trains a policy to maximize a combination of expected reward and policy entropy.
Maximum pseudolikelihood estimation - Maximum pseudolikelihood estimation fits model parameters by maximizing a product of conditional probabilities, often one variable conditional on all the...
Metric multidimensional scaling - Metric multidimensional scaling represents objects as points in a chosen-dimensional space so their distances approximate supplied numerical dissimilarities.
Metropolis-adjusted Langevin algorithm - The Metropolis-adjusted Langevin algorithm, or MALA, is a Markov chain Monte Carlo method that uses gradients of a log target density to guide proposals...
Metropolis within Gibbs - Metropolis within Gibbs is a Markov chain Monte Carlo strategy that updates variables or blocks conditionally, using a Metropolis - Hastings step for...
Minimum norm estimation - Minimum norm estimation selects an estimate with the smallest specified norm among solutions satisfying a fitting criterion.
Mixture of conjugate priors - A mixture of conjugate priors is a weighted combination of prior distributions that are individually conjugate to the same likelihood.
Mixture of factor analyzers - A mixture of factor analyzers is a probabilistic model that combines several factor-analysis components.
Mode connectivity - Mode connectivity describes the existence of a continuous path between trained model parameter settings along which a specified loss remains low.
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...
Monte Carlo dropout - Monte Carlo dropout estimates predictions and model uncertainty by running a dropout-trained neural network repeatedly with dropout active at prediction time.
Monte Carlo expectation maximization - Monte Carlo expectation maximization, or MCEM, replaces an analytically difficult expectation in the EM algorithm with an estimate obtained through simulation.
Multimodal variational autoencoder - A multimodal variational autoencoder is a generative model that learns latent representations from more than one type of data, such as images, text, or...
Multinomial resampling - Multinomial resampling replaces a weighted collection of particles with equally weighted copies selected randomly according to the original weights.
Multivariate Student distribution - The multivariate Student distribution, usually called the multivariate Student-t distribution, is a probability distribution for a vector of continuous...
Multiview representation learning - Multiview representation learning learns useful features by considering two or more related descriptions of the same objects, events, or observations.
N-pairs loss - N-pairs loss is a family of training objectives and batch constructions for learning similarity-based representations.
Natural evolution strategy - A natural evolution strategy is a member of a family of optimization methods that improve a parameterized search distribution using a natural gradient.
Negative-free representation learning - Negative-free representation learning learns useful features without explicitly treating other examples as negative pairs that must be pushed apart.
Nested partition model - In the CrossCat-style setting used here, a nested partition model groups the columns of a data table into views and assigns a separate partition of the rows...
Nested sequential Monte Carlo - Nested sequential Monte Carlo, or NSMC, uses Monte Carlo procedures inside a sequential Monte Carlo algorithm to construct proposals that would otherwise be...
Nesterov accelerated gradient - Nesterov accelerated gradient, often abbreviated NAG, is a first-order optimization method that combines momentum with a gradient evaluated at an...
Neural autoregressive density estimator - A neural autoregressive density estimator models a joint distribution as a product of conditional distributions predicted by neural networks.
Neural-linear approximation - A neural-linear approximation combines a neural feature extractor with a Bayesian linear output model.
Neural process - A neural process is a learned probabilistic model that uses a set of observed input-output pairs to predict outputs at other inputs.
Neural tangent kernel - The neural tangent kernel (NTK) measures how similarly a neural network's outputs at two inputs respond to small changes in its trainable parameters.
Next sentence prediction - Next sentence prediction (NSP) is a self-supervised classification task that asks whether a supplied second text segment follows a first segment in the...
Nguyen-Wainwright-Jordan lower bound - The Nguyen-Wainwright-Jordan (NWJ) lower bound is a variational expression that bounds Kullback-Leibler divergence from below using a real-valued scoring...
No-U-turn sampler - The No-U-turn sampler (NUTS) is a Hamiltonian Monte Carlo method that adapts the length of each simulated trajectory instead of requiring a fixed number of...
Noise-contrastive estimation - Noise-contrastive estimation (NCE) learns a statistical model by training a classifier to distinguish observed data from samples drawn from a known noise...
Noncentered parameterization - A noncentered parameterization expresses a model's latent quantities through standardized auxiliary variables and a transformation, rather than sampling...
Nonmetric multidimensional scaling - Nonmetric multidimensional scaling (NMDS) represents objects as points in a low-dimensional space while trying to preserve the ordering of their pairwise...
Nonstationary kernel - A nonstationary kernel is a covariance function whose value can change when both inputs are translated by the same amount.
Normalizing-flow Kalman filter - A normalizing-flow Kalman filter combines a linear Gaussian state-space model with an invertible transformation of its noisy observations.
One-standard-error rule - The one-standard-error rule selects a simpler model whose estimated validation error is no more than one estimated standard error above the smallest...
Online expectation maximization - Online expectation maximization (online EM) updates a latent-variable model as observations arrive, rather than repeatedly processing the entire dataset in...