What does "variational" mean? - Cross Validated
Apr 17, 2018 · Does the use of "variational" always refer to optimization via variational inference? Examples: "Variational auto-encoder" "Variational Bayesian methods" "Variational renormalization …
deep learning - When should I use a variational autoencoder as …
Jan 22, 2018 · I understand the basic structure of variational autoencoder and normal (deterministic) autoencoder and the math behind them, but when and why would I prefer one type of autoencoder to …
bayesian - What are variational autoencoders and to what learning …
Jan 6, 2018 · Even though variational autoencoders (VAEs) are easy to implement and train, explaining them is not simple at all, because they blend concepts from Deep Learning and Variational Bayes, …
Understanding the Evidence Lower Bound (ELBO) - Cross Validated
Jun 24, 2022 · I am reading this tutorial about Variational Inference, which includes the following depiction of ELBO as the lower bound on log-likelihood on the third page. In the tutorial, $x_i$ is the …
How to weight KLD loss vs reconstruction loss in variational auto …
Mar 7, 2018 · How to weight KLD loss vs reconstruction loss in variational auto-encoder? Ask Question Asked 7 years, 9 months ago Modified 2 years, 3 months ago
regression - What is the difference between Variational Inference and ...
Jul 13, 2022 · I have been reading about variational inference and it is relation to Bayesian regression. It seems there are two versions The first version is discussed here. The second version is discussed …
machine learning - Variational Autoencoder: balance KL-Divergence …
Dec 25, 2020 · They are pretty similar than the ones at this thread: Balancing Reconstruction vs KL Loss Variational Autoencoder but I still have the problems and I don't really understand them, that's why I …
variational bayes - Why don’t diffusion models suffer posterior ...
Apr 16, 2024 · A answer to help provide clarification on posterior collapse, why it happens in the training of VAEs and how these ideas relate to diffusion models. As a first step in understanding posterior …
What's a mean field variational family? - Cross Validated
Feb 10, 2019 · Right now, this centrs on the idea of a mean field variational family. Specifically, Blei et al. say the following: In this review we focus on the mean-field variational family, where the latent …
Which is the best way to implement variational inference?
Jul 25, 2023 · To implement variational inference in a Bayesian model, one essentially has the choice between different approaches that differ in their degree of automation and flexibility: manually …