12 citations · 13 across the 8 of their papers we have counts for
8 papers
Hyperiax and Phylogenetic Inference from Shape Data
Gefan Yang, Marcus Teller, Christy Hipsley +2
Phylogenetic inference on high-dimensional morphological traits requires algorithms that account for both the nonlinear geometry of the shape data and the phylogenetic tree structu…
Neural Backward Filtering Forward Guiding
Gefan Yang, Frank van der Meulen, Stefan Sommer
Inference in nonlinear continuous stochastic processes on trees is challenging, particularly when observations are sparse and the topology is complex. Exact smoothing via Doob's $h…
Stochastics of shapes and Kunita flows
Stefan Sommer, Gefan Yang, Elizabeth Louise Baker
Stochastic processes of evolving shapes are used in applications including evolutionary biology, where morphology changes stochastically as a function of evolutionary processes. Du…
Neural Guided Diffusion Bridges
Gefan Yang, Frank van der Meulen, Stefan Sommer
We propose a novel method for simulating conditioned diffusion processes (diffusion bridges) in Euclidean spaces. By training a neural network to approximate bridge dynamics, our a…
Parameter Inference via Differentiable Diffusion Bridge Importance Sampling
Nicklas Boserup, Gefan Yang, Michael Lind Severinsen +2
We introduce a methodology for performing parameter inference in high-dimensional, non-linear diffusion processes. We illustrate its applicability for obtaining insights into the e…
Infinite-dimensional Diffusion Bridge Simulation via Operator Learning
Gefan Yang, Elizabeth Louise Baker, Michael L. Severinsen +2
The diffusion bridge, which is a diffusion process conditioned on hitting a specific state within a finite period, has found broad applications in various scientific and engineerin…
Conditioning non-linear and infinite-dimensional diffusion processes
Elizabeth Louise Baker, Gefan Yang, Michael L. Severinsen +2
Generative diffusion models and many stochastic models in science and engineering naturally live in infinite dimensions before discretisation. To incorporate observed data for stat…
A Denoising Diffusion Model for Fluid Field Prediction
Gefan Yang, Stefan Sommer
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 compl…