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Variational Autoencoders

Paul Hand via YouTube

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Overview

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This lecture explains variational autoencoders as generative models, covering latent codes, autoencoder architecture, the variational lower bound, KL-divergence regularization, and stochastic optimization.

Syllabus

Introduction
What is agenerative model
Latent code
Autoencoders
likelihood optimization
generative model
nosy observation model
setup
lower bound
KL divergence
Regularization
Maximizing the Lower Bound
Multistep Optimization
Variational Autoencoders
Stochastic Gradient Optimization
Key Points

Taught by

Paul Hand

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