All Outputs (5)

Denoising Diffusion Probabilistic Models on SO(3) for Rotational Alignment (2022)
Presentation / Conference Contribution
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)
Presentation / Conference Contribution
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)
Presentation / Conference Contribution
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.

Optimal scaling of random-walk Metropolis algorithms using Bayesian large-sample asymptotics (2022)
Journal Article
Schmon, S. M., & Gagnon, P. (2022). Optimal scaling of random-walk Metropolis algorithms using Bayesian large-sample asymptotics. Statistics and Computing, 32(2), Article 28. https://doi.org/10.1007/s11222-022-10080-8

High-dimensional limit theorems have been useful to derive tuning rules for finding the optimal scaling in randomwalk Metropolis algorithms. The assumptions under which weak convergence results are proved are however restrictive: the target density i... Read More about Optimal scaling of random-walk Metropolis algorithms using Bayesian large-sample asymptotics.

Learning Multimodal VAEs Through Mutual Supervision (2021)
Presentation / Conference Contribution
Joy, T., Shi, Y., Torr, P. H., Rainforth, T., Schmon, S. M., & Siddharth, N. (2022). Learning Multimodal VAEs Through Mutual Supervision.

Multimodal VAEs seek to model the joint distribution over heterogeneous data (e.g.\ vision, language), whilst also capturing a shared representation across such modalities. Prior work has typically combined information from the modalities by reconcil... Read More about Learning Multimodal VAEs Through Mutual Supervision.