2 papers
cs.LG2024
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
Yifan Chen, Mark Goldstein, Mengjian Hua +3
We propose a framework for probabilistic forecasting of dynamical systems based on generative modeling. Given observations of the system state over time, we formulate the forecasti…
cs.CV2024
SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
Nanye Ma, Mark Goldstein, Michael S. Albergo +3
We present Scalable Interpolant Transformers (SiT), a family of generative models built on the backbone of Diffusion Transformers (DiT). The interpolant framework, which allows for…