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Variational Inference with R

Curriculum

  • 11 Sections
  • 32 Lessons
  • Lifetime
Expand all sectionsCollapse all sections
  • Administration
    1
    • 1.1
      Administration
  • Intro
    3
    • 2.1
      Intuition – what is VI?
    • 2.2
      Notebook – Intuition
    • 2.3
      Origin, Outline, Context
  • KL Divergence
    5
    • 3.1
      KL Introduction
    • 3.2
      KL – Extra Intuition
    • 3.3
      Notebook – KL – Exercises
    • 3.4
      Notebook – KL – Additional Topics
    • 3.5
      KL vs. Other Metrics
  • VI (using KL) vs. ML
    1
    • 4.1
      VI (using KL) vs. ML
  • ELBO & "Mean Field"
    2
    • 5.1
      ELBO
    • 5.2
      “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
    • 6.6
      Notebook – Bayesian GMM – Unknown Precision
    • 6.7
      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
    • 11.1
      Variational Auto Encoder – Theory
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Notebook – ADVI Example (using STAN)
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Notebook – BBVI Example
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