Sitzmann: generative models are the best tools to measure information, but lack compact representations
vincesitzmann · x · 2026-10-20
MIT's Vincent Sitzmann teases a new collaboration with Alan Baade, Gordon Wetzstein, Eric Chan, Fei-Fei Li, Jon Barron and Kyle Sargent, and lays out a technical viewpoint: there's a clear relationship between representation learning and generative modeling.
His core argument: generative models can be used to measure information and mutual information, and are arguably the best tools ever developed for that purpose — but they don't consolidate that information into a compact representation. He says more exciting work is coming.
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