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 kyunghyuncho.me
Stochastic variational inference for low-rank stochastic block models, or how i re-discovered SBM unnecessarily - Kyunghyun Cho
| | tiao.io
A Tutorial on Variational Autoencoders with a Concise Keras Implementation | Louis Tiao
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| | An in-depth practical guide to variational encoders from a probabilistic perspective.
| | 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.
| | www.depthfirstlearning.com
Variational Inference with Normalizing Flows ยท Depth First Learning
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| | [AI summary] The user has provided a detailed and complex set of questions and reading materials related to normalizing flows, variational inference, and generative models. The content covers topics such as the use of normalizing flows to enhance variational posteriors, the inference gap, and the implementation of models like NICE and RealNVP. The user is likely seeking guidance on how to approach these questions, possibly for academic or research purposes.
| | wtfleming.github.io
Cats vs Dogs - Part 3 - 99.1% Accuracy - Binary Image Classification with PyTorch and an Ensemble of ResNet Models
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| [AI summary] This post discusses achieving 99.1% accuracy in binary image classification of cats and dogs using an ensemble of ResNet models with PyTorch.