From the 1 of 4 linked papers with an AI index.
4 papers
The Score Hamiltonian: Mapping Diffusion Models to Adiabatic Transport
Peter Halmos, Boris Hanin
The paper establishes an exact link between score‑based diffusion model sampling and adiabatic transport of ground states of specially constructed Schrödinger operators, providing…
Transport Clustering: Solving Low-Rank Optimal Transport via Clustering
Henri Schmidt, Peter Halmos, Ben Raphael
Optimal transport (OT) finds a least cost transport plan between two probability distributions using a cost matrix defined on pairs of points. Unlike standard OT, which infers unst…
Implicit Bias of the JKO Scheme
Peter Halmos, Boris Hanin
Wasserstein gradient flow provides a general framework for minimizing an energy functional over the space of probability measures on a Riemannian manifold . Its canonica…
Hierarchical Refinement: Optimal Transport to Infinity and Beyond
Peter Halmos, Julian Gold, Xinhao Liu +1
Optimal transport (OT) has enjoyed great success in machine learning as a principled way to align datasets via a least-cost correspondence, driven in large part by the runtime effi…