Deep Generative Models and Inverse Problems

 This is a talk by Alexandros Dimakis given in April 2020 for a seminar at the Institute for Advanced Study seminar on theoretical machine learning.

So he organizes the world of modeling high dimensionality distributions into 3 categories.

1:  Sparsity (so think wavelets).

2:  Conditional Independence  (so think markov chains, factor graphs, bayes nets used in things like channel coding).

3:  Deep Generative Models (so think passing random noise through a learnable differentiable function (like GANS, VAEs, etc).


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