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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 ...
| | 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.
| | blog.evjang.com
Eric Jang: Tips for Training Likelihood Models
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| This is a tutorial on common practices in training generative models that optimize likelihood directly, such as autoregressive models and ...