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 …
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 …
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 …
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, 7 months ago Modified 2 years, 1 month ago
regression - What is the difference between Variational Inference …
Jul 13, 2022 · Many methods proposed for variational inference on latent variable problems alternate between optimizing $\eta_z$ for fixed $\eta_\theta$ and then vice versa, what are …
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, $x_i$ …
How should I intuitively understand the KL divergence loss in ...
How should I intuitively understand the KL divergence loss in variational autoencoders? [duplicate] Ask Question Asked 6 years, 8 months ago Modified 6 years ago
Loss function autoencoder vs variational-autoencoder or MSE-loss …
Jun 7, 2018 · Where as the tensorflow tutorial for variational autoencoder uses binary cross-entropy for measuring the reconstruction loss. Can some please tell me WHY, based on the …
Prior in variational autoencoders - Cross Validated
May 1, 2022 · I am currently dealing with variational autoencoders where I've read the original paper "An introduction to variational Bayes" from Kingma and Welling. I am …
Difference between stochastic variational inference and variational ...
Feb 5, 2018 · Have a look at the paper Stochastic Variational Inference: The coordinate ascent algorithm in Figure 3 is inefficient for large data sets because we must optimize the local …