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An Introduction toVariationalAutoencoders Diederik P. KingmaMax Welling2019 год

An Introduction to Variational Autoencoders
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Полный текстСтраница публикацииПубликация в OpenAlex
Аннотация: Variational autoencoders provide a principled framework for learning deep latent-variable models and corresponding inference models.In this work, we provide an introduction to variational autoencoders and some important extensions.
Год издания: 2019
Авторы: Diederik P. Kingma, Max Welling
Издательство: Now Publishers
Источник: Foundations and Trends® in Machine Learning
Ключевые слова: Generative Adversarial Networks and Image Synthesis, Gaussian Processes and Bayesian Inference, Model Reduction and Neural Networks
Другие ссылки: Foundations and Trends® in Machine Learning (HTML)
arXiv (Cornell University) (PDF)
arXiv (Cornell University) (HTML)
arXiv (Cornell University) (PDF)
arXiv (Cornell University) (HTML)
DataCite API (HTML)