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 proceedings.neurips.cc
Structured Denoising Diffusion Models in Discrete State-Spaces
| | angusturner.github.io
Diffusion Models as a kind of VAE | Angus Turner
1.9 parsecs away

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| | Machine Learning and Data Science.
| | jaketae.github.io
From ELBO to DDPM - Jake Tae
2.7 parsecs away

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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.
| | christopher-beckham.github.io
Techniques for label conditioning in Gaussian denoising diffusion models | Christopher Beckham, PhD
1.3 parsecs away

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| | Techniques for label conditioning in Gaussian denoising diffusion models
| | vxlabs.com
Variational Autoencoder in PyTorch, commented and annotated. - vxlabs
11.4 parsecs away

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| I have recently become fascinated with (Variational) Autoencoders and with PyTorch. Kevin Frans has a beautiful blog post online explaining variational autoencoders, with examples in TensorFlow and, importantly, with cat pictures. Jaan Altosaar's blog post takes an even deeper look at VAEs from both the deep learning perspective and the perspective of graphical models. Both of these posts, as well as Diederik Kingma's original 2014 paper Auto-Encoding Variational Bayes, are more than worth your time.