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 …

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 …

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 …

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, $x_i$ …

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 …

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 …

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 …

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 …

How should I intuitively understand the KL divergence loss in variational autoencoders? [duplicate] Ask Question Asked 6 years, 10 months ago Modified 6 years, 2 months ago

Sep 29, 2024 · Variational Bayesian inference with normalizing flows seems like a potential solution, but it requires a good ansatz for the posterior, which is challenging to obtain due to …