T. Anderson Keller

We introduce a nonlinear score-based generative model operating on periodic domains by leveraging Kuramoto dynamics for orientation-rich data.

We introduce sequence models that are equivariant to time-parameterized symmetries such as motion.

Introduced a new SSM that is maximally expressive and scalable to long sequence modeling tasks