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 …
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" …
Difference between stochastic variational inference and variational ...
The coordinate ascent algorithm in Figure 3 is inefficient for large data sets because we must optimize the local variational parameters for each data point before re-estimating the global …
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 …
machine learning - Variational inference versus MCMC: when to …
Apr 4, 2017 · Thus, variational inference is suited to large data sets and scenarios where we want to quickly explore many models; MCMC is suited to smaller data sets and scenarios where we …
How to Resolve Variational Autoencoder (VAE) Model Collapse in ...
Jul 10, 2023 · I am currently experiencing a suspected model collapse in a Variational Autoencoder (VAE) model I am working with. Below are details on the project setup and the …