What does "variational" mean? - Cross Validated
Apr 17, 2018 · To precisely answer the question what does "variational" mean, we first review the origins of variational inference. By this approach, we gain a broader understanding of the term's meaning. …
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, …
regression - What is the difference between Variational Inference and ...
Jul 13, 2022 · Many methods proposed for variational inference on latent variable problems alternate between optimizing ηz η z for fixed ηθ η θ and then vice versa, what are known in optimization as …
How to do dimension reduction from a variational autoencoder
Dec 19, 2023 · I am thinking about a variational autoencoder. As far as I understand it, in the encoding section you compress to a px1 tensor and then you create a $\\mu$ and $\\sigma$ of dimensions of …
Help Understanding Reconstruction Loss In Variational Autoencoder
Help Understanding Reconstruction Loss In Variational Autoencoder Ask Question Asked 8 years ago Modified 5 years, 6 months ago
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 currently still a little …
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, 10 months ago Modified 2 years, 4 months ago
Understanding the set of latent variables $Z$ in variational inference
Mar 4, 2021 · Hence in the setting of variational inference, you may often observe a parity between the number of latent variables being used in the model, and the number of observations, as stated by …
variational bayes - Why don’t diffusion models suffer posterior ...
Apr 16, 2024 · In VAEs, posterior collapse occurs when the approximated posterior qθ(z|x) q θ (z | x) becomes the standard Gaussian prior p(z) p (z) after training (Lucas et al. 2019). The forward …