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 is a single lecture from a course. If you you like the material and want more context (e.g., the lectures that came before), check ...
This is the twentyfourth lecture in the Probabilistic ML class of Prof. Dr. Philipp Hennig, updated for the Summer Term 2021 at the ...
Summary & Highlights for Variational Inference By Automatic Differentiation In Tensorflow Probability
- This short tutorial covers the basics of
- Inference of probabilistic models using
- TensorFlow Probability
- David Blei, Columbia University Computational Challenges in Machine Learning ...
- David Blei, Rajesh Ranganath, Shakir Mohamed. One of the core problems of modern statistics and machine learning is to ...
That wraps up our extensive overview of Variational Inference By Automatic Differentiation In Tensorflow Probability.
