37 citations · 63 across the 8 of their papers we have counts for
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cs.LG2024
GUD: Generation with Unified Diffusion
Mathis Gerdes, Max Welling, Miranda C. N. Cheng
Diffusion generative models transform noise into data by inverting a process that progressively adds noise to data samples. Inspired by concepts from the renormalization group in p…
hep-lat2024★ 3 cited
Non-Perturbative Trivializing Flows for Lattice Gauge Theories
Mathis Gerdes, Pim de Haan, Roberto Bondesan +1
Continuous normalizing flows are known to be highly expressive and flexible, which allows for easier incorporation of large symmetries and makes them a powerful computational tool…