2 papers
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-th2020
Moduli-dependent Calabi-Yau and SU(3)-structure metrics from Machine Learning
Lara B. Anderson, Mathis Gerdes, James Gray +3
We use machine learning to approximate Calabi-Yau and SU(3)-structure metrics, including for the first time complex structure moduli dependence. Our new methods furthermore improve…