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stat.ML2026
Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling
Robert Gruhlke, Julius Berner, David Sommer +1
Diffusion models offer a powerful framework for sampling from complex probability densities by learning to reverse a noising process. A common approach involves solving for the tim…
stat.ML2024
Generative Modelling with Tensor Train approximations of Hamilton--Jacobi--Bellman equations
David Sommer, Robert Gruhlke, Max Kirstein +2
Sampling from probability densities is a common challenge in fields such as Uncertainty Quantification (UQ) and Generative Modelling (GM). In GM in particular, the use of reverse-t…