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 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)
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| | 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 ...
| | lilianweng.github.io
What are Diffusion Models? | Lil'Log
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| | [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.
| | 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.
| | web.navan.dev
Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria
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| Tutorial on creating an image classifier model using TensorFlow which detects malaria