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 www.depthfirstlearning.com
Variational Inference with Normalizing Flows ยท Depth First Learning
| | lilianweng.github.io
Flow-based Deep Generative Models | Lil'Log
1.5 parsecs away

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| | So far, I've written about two types of generative models, GAN and VAE. Neither of them explicitly learns the probability density function of real data, $p(\mathbf{x})$ (where $\mathbf{x} \in \mathcal{D}$) - because it is really hard! Taking the generative model with latent variables as an example, $p(\mathbf{x}) = \int p(\mathbf{x}\vert\mathbf{z})p(\mathbf{z})d\mathbf{z}$ can hardly be calculated as it is intractable to go through all possible values of the latent code $\mathbf{z}$.
| | tiao.io
A Tutorial on Variational Autoencoders with a Concise Keras Implementation | Louis Tiao
0.8 parsecs away

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| | An in-depth practical guide to variational encoders from a probabilistic perspective.
| | jxmo.io
Introduction to variational autoencoders - Jack Morris
0.7 parsecs away

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| | A primer on variational autoencoders (VAEs) culminating in a PyTorch implementation of a VAE with discrete latents.
| | polukhin.tech
ChatGPT in Machine Learning | Andrii Polukhin
15.5 parsecs away

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| A robot sitting next to a human in an office, trending on artstation, beautiful coloring, 4k, vibrant, blue and yellow, by DreamStudio