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Inference for Latent Variable Energy-Based Models (LV-EBMs)

Difficulty level

This lecture covers the concept of inference in latent variable energy based models (LV-EBMs) and is a part of the Deep Learning Course at NYU's Center for Data Science. 

Topics covered in this lesson

00:00 – Affine transformation in 2 and 3D by @LeiosOS (James Schloss)
01:21 – Thanks for sending me a Wacom graphic tablet
01:50 – *Inference* for LV EBM (we're given a model)
04:32 – Training samples: one to many mapping
13:10 – Let's simplify stuff: the unconditional case
15:56 – Untrained model manifold generation
21:15 – The Energy Function
24:51 – Indexing energy function by picking individual training samples
31:41 – The 23rd energy (U shaped)
39:27 – The 10th energy (~ shaped)
46:07 – The Free Energy (definition and the 10th example)
51:59 – The 23rd free energy
53:07 – Computing the free energy for the entire 𝒴 space
1:00:01 – That was it :)