Generative Modeling: A Review

2024/12/24 by Nareklishvili, Maria, Polson, Nick, Sokolov, Vadim · 1 citation
#Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2501.05458

Abstract

Generative methods (Gen-AI) are reviewed with a particular goal of solving tasks in machine learning and Bayesian inference. Generative models require one to simulate a large training dataset and to use deep neural networks to solve a supervised learning problem. To do this, we require high-dimensional regression methods and tools for dimensionality reduction (a.k.a. feature selection). The main advantage of Gen-AI methods is their ability to be model-free and to use deep neural networks to estimate conditional densities or posterior quintiles of interest. To illustrate generative methods , we analyze the well-known Ebola data set. Finally, we conclude with directions for future research.

Cited by

• Generative Bayesian Hyperparameter Tuning

Related

• A Brief Introduction to Generative Models
• Generative Models: An Interdisciplinary Perspective
• Generative AI for Bayesian Computation
• Generative adversarial networks
• A Review of Learning with Deep Generative Models from Perspective of Graphical Modeling