YoVDO

Probabilistic Flow Circuits: Towards Deep Models for Tractable Inference - Oral Session 6

Offered By: Uncertainty in Artificial Intelligence via YouTube

Tags

Probabilistic Circuits Courses Artificial Intelligence Courses Machine Learning Courses Statistical Modeling Courses Probability Theory Courses Generative Models Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore a 27-minute conference talk from the Uncertainty in Artificial Intelligence (UAI) 2023 Oral Session 6 that introduces Probabilistic Flow Circuits, a novel approach to increasing the expressivity of probabilistic circuits. Delve into the theoretical foundations and practical implications of combining probabilistic circuits with normalizing flows to create more powerful yet tractable probabilistic models. Learn how the researchers establish the requirement of decomposability for maintaining tractability in these hybrid models. Examine the empirical evaluation that demonstrates the enhanced expressivity and tractability of Probabilistic Flow Circuits, which extend traditional circuits by incorporating normalizing flows at the leaf nodes. Gain insights into this innovative approach to unified deep models for tractable probabilistic inference, presented by Sahil Sidheekh, Kristian Kersting, and Sriraam Natarajan.

Syllabus

UAI 2023 Oral Session 6: Probabilistic Flow Circuits: Towards Deep Models for Tractable Inference


Taught by

Uncertainty in Artificial Intelligence

Related Courses

Introduction to Artificial Intelligence
Stanford University via Udacity
Probabilistic Graphical Models 1: Representation
Stanford University via Coursera
Artificial Intelligence for Robotics
Stanford University via Udacity
Computer Vision: The Fundamentals
University of California, Berkeley via Coursera
Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent