YoVDO

Topology of Surprisal - Information Theory and Vietoris-Rips Filtrations

Offered By: Applied Algebraic Topology Network via YouTube

Tags

Information Theory Courses Deep Learning Courses Topology Courses Kullback-Leibler Divergence Courses

Course Description

Overview

Explore the intersection of information theory and topological data analysis in this insightful lecture on combining Kullback-Leibler divergence with Vietoris-Rips filtrations. Delve into the challenges of using the non-symmetric Kullback-Leibler divergence as a distance measure in topological constructions and discover an innovative approach to overcome this limitation. Learn about the concept of surprisal in probability distributions and its applications in deep learning. Gain valuable insights from the work of Hubert Wagner, Herbert Edelsbrunner, and Ziga Virk as they present a novel method for integrating information-theoretical distance measures with topological data analysis techniques.

Syllabus

Hubert Wagner (4/14/23): Topology of... surprisal: Information theory and Vietoris-Rips filtrations


Taught by

Applied Algebraic Topology Network

Related Courses

Trust Region & Proximal Policy Optimization
Pascal Poupart via YouTube
Evaluation of Adaptive Systems
Association for Computing Machinery (ACM) via YouTube
The Key Equation Behind Probability - Entropy, Cross-Entropy, and KL Divergence
Artem Kirsanov via YouTube
Variational Inference and Optimization - Lecture 1
Probabilistic AI School via YouTube
New Directions in Quantum State Learning and Testing
QuICS via YouTube