Apr 17, 2018 · Does the use of "variational" always refer to optimization via variational inference? Examples: "Variational auto-encoder" "Variational Bayesian methods" "Variational renormalization …

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 …

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, …

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 currently still a little …

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 …

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 …

Jul 16, 2024 · The SDs of the inferred variational beliefs can indeed be very small, if the network is simply very certain about the values of the latents given the input. For instance, it may be very …

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 …

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