Variational Inference

Summary

In real-world applications, the posterior over the latent variables Z given some data D is usually intractable. But we can use a ... ... community: patreon.com/artemkirsanov ===== In this video, we explore For more information about Stanford's Artificial Intelligence programs visit: stanford.io/ai To along with the course, ... David Blei, Rajesh Ranganath, Shakir Mohamed. One of the core problems of modern statistics and machine learning is to ... In this video I will try to give the basic intuition of what VI is. The first and only online pydata.org When Bayesian modeling scales up to large datasets, traditional MCMC methods can become impractical due to ... David Blei, Columbia University Computational Challenges in Machine Learning ... ... different parts of the theory behind VAEs: - Variational Autoencoders mbernste.github.io/posts/vae/ - This is Lecture 23 of the course on Probabilistic Machine Learning in the Summer Term of 2025 at the University of Tübingen, ... VI attempts to find an optimal surrogate posterior by maximizing the Evidence Lower Bound (=ELBO). The surrogate posterior acts ... ... usual instead of covering any new reinforcement learning algorithms we're actually going to talk about Inference of probabilistic models using Speaker: Max Welling (University of Amsterdam) ... are in generative modeling source compensation