All Outputs (3)

Denoising Diffusion Probabilistic Models on SO(3) for Rotational Alignment (2022)
Conference Proceeding
Leach, A., Schmon, S. M., Degiacomi, M. T., & Willcocks, C. G. (2022). Denoising Diffusion Probabilistic Models on SO(3) for Rotational Alignment.

Probabilistic diffusion models are capable of modeling complex data distributions on high-dimensional Euclidean spaces for a range applications. However, many real world tasks involve more complex structures such as data distributions defined on mani... Read More about Denoising Diffusion Probabilistic Models on SO(3) for Rotational Alignment.

AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise (2022)
Conference Proceeding
Wyatt, J., Leach, A., Schmon, S. M., & Willcocks, C. G. (2022). AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise. . https://doi.org/10.1109/cvprw56347.2022.00080

Generative models have been shown to provide a powerful mechanism for anomaly detection by learning to model healthy or normal reference data which can subsequently be used as a baseline for scoring anomalies. In this work we consider denoising diffu... Read More about AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise.

Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation (2022)
Conference Proceeding
Dyer, J., Cannon, P., Schmon, S. M., Camps-Valls, G., Ruiz, F. J., & Valera, I. (2022). Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation.

Simulation models of complex dynamics in the natural and social sciences commonly lack a tractable likelihood function, rendering traditional likelihood-based statistical inference impossible. Recent advances in machine learning have introduced novel... Read More about Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation.