Outer Web | Explore

/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		kyunghyuncho.me Stochastic variational inference for low-rank stochastic block models, or how i re-discovered SBM unnecessarily - Kyunghyun Cho
\|	\|	blog.fastforwardlabs.com Under the Hood of the Variational Autoencoder (in Prose and Code) The Variational Autoencoder (VAE) neatly synthesizes unsupervised deep learning and variational Bayesian methods into one sleek package. In Part I of this series, we introduced the theory and intuition behind the VAE, an exciting development in machine learning for combined generative modeling and inference-"machines that imagine and reason." To recap: VAEs put a probabilistic spin on the basic autoencoder paradigm-treating their inputs, hidden representations, and reconstructed outputs as probabilistic ...	3.8 parsecs Travel
\|	\|
\|	\|	lilianweng.github.io What are Diffusion Models? \| Lil'Log [Updated on 2021-09-19: Highly recommend this blog post on score-based generative modeling by Yang Song (author of several key papers in the references)]. [Updated on 2022-08-27: Added classifier-free guidance, GLIDE, unCLIP and Imagen. [Updated on 2022-08-31: Added latent diffusion model. [Updated on 2024-04-13: Added progressive distillation, consistency models, and the Model Architecture section.	3.7 parsecs Travel
\|	\|
\|	\|	jxmo.io Introduction to variational autoencoders - Jack Morris A primer on variational autoencoders (VAEs) culminating in a PyTorch implementation of a VAE with discrete latents.	2.8 parsecs Travel
\|	\|
\|	\|	bdtechtalks.com Machine learning: What is the transformer architecture? - TechTalks The transformer model has become one of the main highlights of advances in deep learning and deep neural networks.	15.3 parsecs Travel
\|