Variational Auto Encoder (VAE) - Theory

VAE's are a mix between VI and Auto Encoders NN. They are used mainly for generating new data. In this video we will outline the theory behind the original paper, including looking at regular Auto Encoders, Variational Inference, and how they mix together to create VAE.

Original Paper (Kingma & Welling 2014): https://arxiv.org/pdf/1312.6114

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“VI in R” Course Outline:
Administration
Administration
Intro
Intuition – what is VI?
Notebook – Intuition
Origin, Outline, Context
KL Divergence
KL Introduction
KL – Extra Intuition
Notebook – KL – Exercises
Notebook – KL – Additional Topics
KL vs. Other Metrics
VI vs. ML
VI (using KL) vs. Maximum Likelihood
ELBO & “Mean Field”
ELBO
“Mean Field” Approximation
Coordinate Ascent VI (CAVI)
Coordinate Ascent VI (CAVI)
Functional Derivative & Euler-Lagrange Equation
CAVI – Toy Example
CAVI – Bayesian GMM Example
Notebook – Normal-Gamma Conjugate Prior
Notebook – Bayesian GMM – Unknown Precision
Notebook – Image Denoising (Ising Model)
Exponential Family
CAVI for the Exponential Family
Conjugacy in the Exponential Family
Notebook – Latent Dirichlet Allocations Example
VI vs. EM
VI vs. EM
Stochastic VI / Advanced VI
SVI – Review
SVI for Exponential Family
Automatic Differentiation VI (ADVI)
Notebook – ADVI Example (using STAN)
Black Box VI (BBVI)
Notebook – BBVI Example
Expectation Propagation
Forward vs. Reverse KL
Expectation Propagation
Variational Auto Encoder

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Intro/Outro Music: Dreamer - by Johny Grimes
   • Johny Grimes - Dreamer