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 This short tutorial covers the basics of Speaker: Sayam Kumar Title: Demystifying
This is Lecture 23 of the course on Probabilistic Machine Learning in the Summer Term of 2025 at the University of Tübingen, ...
Summary & Highlights for Variational Inference By Automatic Differentiation In Tensorflow Probability
- 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 ...
- TensorFlow Probability
- Probabilistic AI School 2026 Materials: https://github.com/probabilisticai/probai-2026 Hosted by: - Lithuanian Artificial Intelligence ...
- In real-world applications, the posterior over the latent variables Z given some data D is usually intractable. But we can use a ...
- VI attempts to find an optimal surrogate posterior by maximizing the Evidence Lower Bound (=ELBO). The surrogate posterior acts ...
That wraps up our extensive overview of Variational Inference By Automatic Differentiation In Tensorflow Probability.