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 blog.evjang.com
Eric Jang: Normalizing Flows Tutorial, Part 2: Modern Normalizing Flows
| | akosiorek.github.io
Normalizing Flows
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| | Machine learning is all about probability.To train a model, we typically tune its parameters to maximise the probability of the training dataset under the mo...
| | www.depthfirstlearning.com
Variational Inference with Normalizing Flows ยท Depth First Learning
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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.
| | 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.
| | liorsinai.github.io
Image generation with MNIST - Lior Sinai
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| A denoising diffusion probabilistic model for generating numbers based on the MNIST dataset. The underlying machine learning model is a U-Net model, which is...