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Variational Inference with R
Variational Inference with R
Curriculum
11 Sections
32 Lessons
Lifetime
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Administration
1
1.0
Administration
Intro
3
2.1
Intuition – what is VI?
2.2
Notebook – Intuition
2.3
Origin, Outline, Context
KL Divergence
5
3.0
KL Introduction
3.1
KL – Extra Intuition
3.2
Notebook – KL – Exercises
3.3
Notebook – KL – Additional Topics
3.4
KL vs. Other Metrics
VI (using KL) vs. ML
1
4.0
VI (using KL) vs. ML
ELBO & "Mean Field"
2
5.0
ELBO
5.1
“Mean Field” Approximation
Coordinate Ascent VI (CAVI)
7
6.1
Coordinate Ascent VI (CAVI)
6.2
Functional Derivative & Euler-Lagrange
6.3
CAVI – Toy Example
6.4
CAVI – Bayesian GMM Example
6.5
Notebook – Normal-Gamma Conjugate Prior
6.7
Notebook – Bayesian GMM – Unknown Precision
6.8
Notebook – Image denoising (Ising model)
Exponential Family
3
7.1
CAVI for the Exponential Family
7.2
Conjugacy in the Exponential Family
7.3
Notebook – Latent Dirichlet Allocation Example
VI vs. EM
1
8.1
VI vs. EM
Stochastic VI / Advanced VI
6
9.1
SVI – Review
9.2
SVI for Exponential Family
9.3
Automatic Differentiation VI (ADVI)
9.4
Notebook – ADVI Example (using STAN)
9.5
Black Box VI (BBVI)
9.6
Notebook – BBVI Example
Expectation Propagation
2
10.1
Forward vs. Reverse KL
10.2
Expectation Propagation
Variational Auto-Encoder
1
11.0
Variational Auto Encoder – Theory
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