Conditional random field
Conditional random fields (CRFs) are a type of statistical modeling method used in pattern recognition and machine learning, particularly for structured prediction tasks.
probabilistic models
Neural variational inference
Neural variational inference is a machine learning technique that uses neural networks to approximate complex posterior distributions in probabilistic models. It combines variational inference with deep learning to enable scalable and flexible inference in models where exact Bayesian inference is intractable.
Gaussian process (machine learning)
Gaussian processes are a powerful statistical tool used in machine learning for regression and classification tasks, offering probabilistic predictions.
Naive Bayes classifier
The Naive Bayes classifier is a probabilistic machine learning algorithm used for classification tasks based on Bayes’ theorem, assuming independence among predictors.
Energy-based model
An energy-based model is a type of probabilistic model in machine learning that associates a scalar energy value to each configuration of variables. These models learn to represent data by minimizing the energy of observed data points and assigning higher energy to other configurations.
Score-based generative model
A score-based generative model is a type of probabilistic model that generates data by estimating the gradient of the data distribution’s log-density, known as the score function. These models employ techniques from stochastic differential equations and denoising score matching to synthesize high-quality samples, especially in image generation.