ICML 2026 paper FEGF bridges gradient-flow math and ML via JKO conditions
FrancescoLocat8 · x · 2026-10-07
This post is part of a thread introducing FEGF (Free-Energy Gradient Flows), a method by Rancati, Maas and Locatello published at ICML 2026 that bridges gradient-flow mathematics with machine learning.
- FEGF builds on JKO optimality conditions and geodesic preprocessing, following prior work by Terpin, Dörfler and colleagues.
- The concurrently published DAM method stands in contrast by completely avoiding that preprocessing step.
Related event: FEGF Brings Gradient Flow Theory to Graph Discrete Diffusion(2 posts)→
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