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 sander.ai
Diffusion models are autoencoders Sander Dieleman
| | distill.pub
Open Questions about Generative Adversarial Networks
2.2 parsecs away

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| | What we'd like to find out about GANs that we don't know yet.
| | www.depthfirstlearning.com
Variational Inference with Normalizing Flows ยท Depth First Learning
1.6 parsecs away

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| | [AI summary] The user has provided a detailed and complex set of questions and reading materials related to normalizing flows, variational inference, and generative models. The content covers topics such as the use of normalizing flows to enhance variational posteriors, the inference gap, and the implementation of models like NICE and RealNVP. The user is likely seeking guidance on how to approach these questions, possibly for academic or research purposes.
| | tiao.io
A Tutorial on Variational Autoencoders with a Concise Keras Implementation | Louis Tiao
1.3 parsecs away

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| | An in-depth practical guide to variational encoders from a probabilistic perspective.
| | sander.ai
Perspectives on diffusion - Sander Dieleman
2.6 parsecs away

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| Perspectives on diffusion, or how diffusion models are autoencoders, deep latent variable models, score function predictors, reverse SDE solvers, flow-based models, RNNs, and autoregressive models, all at once!