Introduction to Variational Inference By Automatic Differentiation In Tensorflow Probability
Let's dive into the details surrounding Variational Inference By Automatic Differentiation In Tensorflow Probability. We find a surrogate posterior by maximizing the Evidence Lower Bound (ELBO). With a proposal distribution, this can be solved ...
Variational Inference By Automatic Differentiation In Tensorflow Probability Comprehensive Overview
In this video, we break down In real-world applications, the posterior over the latent variables Z given some data D is usually intractable. But we can use a ... This short tutorial covers the basics of
This video is supporting material for the book ...
Summary & Highlights for Variational Inference By Automatic Differentiation In Tensorflow Probability
- David Blei, Rajesh Ranganath, Shakir Mohamed. One of the core problems of modern statistics and machine learning is to ...
- TensorFlow Probability
- Inference of probabilistic models using
- ADVI is an general VI algorithm that applies to problems outside the Expo. Family. It is a form of SVI, it does stochastic gradient ...
- MLFoundations #Calculus #MachineLearning In this video, we use a hands-on code demo in
That wraps up our extensive overview of Variational Inference By Automatic Differentiation In Tensorflow Probability.