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 blog.fastforwardlabs.com
Under the Hood of the Variational Autoencoder (in Prose and Code)
| | jxmo.io
Introduction to variational autoencoders - Jack Morris
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| | A primer on variational autoencoders (VAEs) culminating in a PyTorch implementation of a VAE with discrete latents.
| | kyunghyuncho.me
Amortized Mixture of Gaussians (AMoG): A Proof of Concept for "Learning to X", or how I re-discovered simulation-based inference - Kyunghyun Cho
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| | www.nicktasios.nl
Latent Diffusion Series: Variational Autoencoder (VAE) | NICK TASIOS
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| | In the Latent Diffusion Series of blog posts, I'm going through all components needed to train a latent diffusion model to generate random digits from the MNIST dataset. In the second post, we will bu
| | jaketae.github.io
From ELBO to DDPM - Jake Tae
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| In this short post, we will take a look at variational lower bound, also referred to as the evidence lower bound or ELBO for short. While I have referenced ELBO in a previous blog post on VAEs, the proofs and formulations presented in the post seems somewhat overly convoluted in retrospect. One might consider this a gentler, more refined recap on the topic. For the remainder of this post, I will use the terms "variational lower bound" and "ELBO" interchangeably to refer to the same concept. I was heavily inspired by Hugo Larochelle's excellent lecture on deep belief networks.