Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Yang, Gefan"'
Autor:
Boserup, Nicklas, Yang, Gefan, Severinsen, Michael Lind, Hipsley, Christy Anna, Sommer, Stefan
We introduce a methodology for performing parameter inference in high-dimensional, non-linear diffusion processes. We illustrate its applicability for obtaining insights into the evolution of and relationships between species, including ancestral sta
Externí odkaz:
http://arxiv.org/abs/2411.08993
Autor:
Yang, Gefan, Baker, Elizabeth Louise, Severinsen, Michael L., Hipsley, Christy Anna, Sommer, Stefan
The diffusion bridge is a type of diffusion process that conditions on hitting a specific state within a finite time period. It has broad applications in fields such as Bayesian inference, financial mathematics, control theory, and shape analysis. Ho
Externí odkaz:
http://arxiv.org/abs/2405.18353
Autor:
Baker, Elizabeth Louise, Yang, Gefan, Severinsen, Michael L., Hipsley, Christy Anna, Sommer, Stefan
Generative diffusion models and many stochastic models in science and engineering naturally live in infinite dimensions before discretisation. To incorporate observed data for statistical and learning tasks, one needs to condition on observations. Wh
Externí odkaz:
http://arxiv.org/abs/2402.01434
Autor:
Yang, Gefan, Sommer, Stefan
We propose a novel denoising diffusion generative model for predicting nonlinear fluid fields named FluidDiff. By performing a diffusion process, the model is able to learn a complex representation of the high-dimensional dynamic system, and then Lan
Externí odkaz:
http://arxiv.org/abs/2301.11661