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 sander.ai
Diffusion models are autoencoders Sander Dieleman
| | angusturner.github.io
Diffusion Models as a kind of VAE | Angus Turner
1.3 parsecs away

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| | Machine Learning and Data Science.
| | tiao.io
A Tutorial on Variational Autoencoders with a Concise Keras Implementation | Louis Tiao
1.4 parsecs away

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| | An in-depth practical guide to variational encoders from a probabilistic perspective.
| | yang-song.net
Generative Modeling by Estimating Gradients of the Data Distribution | Yang Song
0.5 parsecs away

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| | This blog post focuses on a promising new direction for generative modeling. We can learn score functions (gradients of log probability density functions) on a large number of noise-perturbed data distributions, then generate samples with Langevin-type sampling. The resulting generative models, often called score-based generative models, has several important advantages over existing model families: GAN-level sample quality without adversarial training, flexible model architectures, exact log-likelihood ...
| | 0fps.net
Conway's Game of Life for Curved Surfaces (Part 2) - 0 FPS
17.9 parsecs away

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| (This is the sequel to the following post on SmoothLife. For background information go there, or read Stephan Rafler's paper on SmoothLife here.) Last time, we talked about an interesting generalization of Conway's Game of Life and walked through the details of how it was derived, and investigated some strategies for discretizing it. Today, let's...