/explore

Click through on any links that interest you or select the planets on the right to continue exploring the Outer Web.
You are here

 akosiorek.github.io
Normalizing Flows
| | jxmo.io
Introduction to variational autoencoders - Jack Morris
2.2 parsecs away

Travel
| | A primer on variational autoencoders (VAEs) culminating in a PyTorch implementation of a VAE with discrete latents.
| | jaketae.github.io
Glow-TTS - Jake Tae
2.5 parsecs away

Travel
| | Note: This blog post was completed as part of Yale's CPSC 482: Current Topics in Applied Machine Learning.
| | iclr-blogposts.github.io
Elaborating on the Value of Flow Matching for Density Estimation | ICLR Blogposts 2024
2.5 parsecs away

Travel
| | The transfer of matching-based training from Diffusion Models to Normalizing Flows allows to fit expressive continuous normalizing flows efficiently and therefore enables their usage for different kinds of density estimation tasks. One particularly interesting task is Simulation-Based Inference, where Flow Matching enabled several improvements. The post shall focus on the discussion of Flow Matching for Continuous Normalizing Flows. To highlight the relevance and the practicality of the method, their use and advantages for Simulation-Based Inference is elaborated.
| | vxlabs.com
Variational Autoencoder in PyTorch, commented and annotated. - vxlabs
6.9 parsecs away

Travel
| 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.