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- What does variational mean? - Cross Validated
Does the use of "variational" always refer to optimization via variational inference? Examples: "Variational auto-encoder" "Variational Bayesian methods" "Variational renormalization group"
- deep learning - When should I use a variational autoencoder as opposed . . .
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 the other? Al
- How to weight KLD loss vs reconstruction loss in variational auto-encoder?
How to weight KLD loss vs reconstruction loss in variational auto-encoder? Ask Question Asked 7 years, 8 months ago Modified 2 years, 2 months ago
- bayesian - What are variational autoencoders and to what learning tasks . . .
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, and the Deep Learning and Probabilistic Modeling communities use different terms for the same concepts Thus when explaining VAEs you risk either concentrating on the statistical model part, leaving the reader
- Understanding the Evidence Lower Bound (ELBO) - Cross Validated
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 observed data and $z_i$ is the latent variable
- regression - What is the difference between Variational Inference and . . .
Many methods proposed for variational inference on latent variable problems alternate between optimizing for fixed and then vice versa, what are known in optimization as methods (and actually oftentimes just plain-ol' coordinate descent, but let's leave that story for another day)
- 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
- Normalizing flows as a generalization of variational autoencoders . . .
For those curious to link the said techniques to more state-of-the-art generative algorithms, diffusion models can be transformed into continuous normalizing flows (CNFs) and interpreted as a specific form of a Markovian Hierarchical Variational Autoencoder The following excerpts are taken from my book on variational inference
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