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www.daniellowengrub.com | ||
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fa.bianp.net
The Langevin algorithm is a simple and powerful method to sample from a probability distribution. It's a key ingredient of some machine learning methods such as diffusion models and differentially private learning. In this post, I'll derive a simple convergence analysis of this method in the special case when the ... |
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healeycodes.com
Generating random but familiar text by building Markov chains from scratch. |
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www.ethanepperly.com
[AI summary] The user is discussing Markov Chain Monte Carlo (MCMC) methods, specifically the Metropolis-Hastings algorithm, applied to sampling from a distribution defined by a matrix $ A $. The focus is on the acceptance probability when transitioning between subsets $ S $ and $ S' $ of size $ k $, where the acceptance probability is determined by the ratio of determinants of submatrices of $ A $. The user is also exploring the computational complexity of these methods and their application to problems involving large matrices. |
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vxlabs.com
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. |
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