Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

On the Critic Function of Implicit Generative Models - Arthur Gretton

Institute for Advanced Study via YouTube

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore the critic function of implicit generative models in this comprehensive seminar on Theoretical Machine Learning. Delve into divergence measures, variational forms, and topological properties as Arthur Gretton from University College London discusses generalized energy-based models and their applications. Examine the advantages and disadvantages of various approaches, including neural net divergence and generalized likelihood. Gain insights into smoothness properties, multimodality, and the challenges of jumping between modes in generative models. Understand the risks of memorization and the importance of realistic sampling in machine learning applications.

Syllabus

Introduction
Outline
Divergence measures
The critic function
Variational form
Lower bound
Topological properties
Disadvantages of kl
Generalized energybased models
The generator
Generalised energybased models
Generalised likelihood
Graphical example
Energy function
Sampling
Realistic
Neural net divergence
How close is Q to P
Will I hit P
Smoothness properties
Jumping from mode to mode
I was happy to see it go from mode to mode
Risk of memorization
Generalized energybased model
Generalized likelihood
Multimodality
Smooth functions
The kernel beer
Mark

Taught by

Institute for Advanced Study

Reviews

Start your review of On the Critic Function of Implicit Generative Models - Arthur Gretton

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.