machine learning - What does "variational" mean? - Cross Validated
Apr 17, 2018 · Variational inference originated in the 18th century with the work of Euler, Lagrange and others studying the field of calculus. In calculus of variations, a function maps …
deep learning - When should I use a variational autoencoder as …
Jan 22, 2018 · To tackle this problem, the variational autoencoder was created by adding a layer containing a mean and a standard deviation for each hidden variable in the middle layer: Then …
bayesian - What are variational autoencoders and to what learning …
Jan 6, 2018 · Enter Variational Inference, the tool which gives Variational Autoencoders their name. Variational Inference for the VAE model. Variational Inference is a tool to perform …
bayesian - Understanding the Evidence Lower Bound (ELBO
Jun 24, 2022 · In the case of variational EM/inference, it is not the case that the lower bound is tight. Therefore, maximizing the lower bound can actually lead to a decrease in the actual log …
regression - What is the difference between Variational Inference …
Jul 13, 2022 · Historically, variational bayes has been popular in applications that involve latent variables. These latent variables are treated identically to parameters in both Bayesian and …
What's a mean field variational family? - Cross Validated
Feb 10, 2019 · To elaborate on and give context to previous answers, we expand on the use of mean-field variational assumptions in machine learning. The question can be decomposed …
Removing noise with Variational Autoencoders - Cross Validated
Feb 7, 2019 · $\begingroup$ @user1533286 then when using relatively small latent dimensionality and/or regularization, the autoencoder should focus only on the "strong" …
Help Understanding Reconstruction Loss In Variational Autoencoder
Variational Autoencoder − Dimension of the latent space. 2. On evaluating variational autoencoders with ...
Comparing Laplace Approximation and Variational Inference
$\begingroup$ It is a limitation of my terminology, but I think what you call variational inference is also called Bayesian inference. Havard Rue at Norway has done work on nested Laplace …
How to weight KLD loss vs reconstruction loss in variational auto …
Mar 7, 2018 · I want to add another interesting paper relating to this question, where the authors propose a cyclical annealing scheme for the KLD term to improve the training of a VAE for …