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All Outputs (10)

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.

Bayesian emulation of computer experiments of infrastructure slope stability models (2022)
Conference Proceeding
Svalova, A., Helm, P., Prangle, D., Rouainia, M., Glendinning, S., & Wilkinson, D. (2022). Bayesian emulation of computer experiments of infrastructure slope stability models. In Proceedings of the 8th International Symposium on Geotechnical Safety and Risk (ISGSR). https://doi.org/10.3850/978-981-18-5182-7_00-07-011.xml

We performed a fully-Bayesian Gaussian process emulation and sensitivity analysis of a numerical model that simulates transport cutting slope deterioration. In the southern UK, a significant proportion of transport infrastructure is built in overcons... Read More about Bayesian emulation of computer experiments of infrastructure slope stability models.

Decision making under severe uncertainty on a budget (2022)
Conference Proceeding
Nakharutai, N., Destercke, S., & Troffaes, M. C. (2022). Decision making under severe uncertainty on a budget. In F. Dupin de Saint-Cyr, M. Öztürk-Escoffier, & N. Potyka (Eds.), . https://doi.org/10.1007/978-3-031-18843-5_13

Convex sets of probabilities are general models to describe and reason with uncertainty. Moreover, robust decision rules defined for them enable one to make cautious inferences by allowing sets of optimal actions to be returned, reflecting lack of in... Read More about Decision making under severe uncertainty on a budget.

The complexity of computing optimum labelings for temporal connectivity (2022)
Conference Proceeding
Klobas, N., Mertzios, G., Molter, H., & Spirakis, P. (2022). The complexity of computing optimum labelings for temporal connectivity. . https://doi.org/10.4230/lipics.mfcs.2022.62

A graph is temporally connected if there exists a strict temporal path, i.e., a path whose edges have strictly increasing labels, from every vertex u to every other vertex v. In this paper we study temporal design problems for undirected temporally c... Read More about The complexity of computing optimum labelings for temporal connectivity.

Numerical simulation of self-dual U(1) lattice field theory with electric and magnetic matter (2022)
Conference Proceeding
Anosova, M., Gattringer, C., Iqbal, N., & Sulejmanpasic, T. (2022). Numerical simulation of self-dual U(1) lattice field theory with electric and magnetic matter. . https://doi.org/10.22323/1.396.0386

We study a recently proposed formulation of U(1) lattice field theory with electric and magnetic matter based on the Villain formulation. This discretization allows for a duality that gives rise to relations between weak and strong gauge coupling. Th... Read More about Numerical simulation of self-dual U(1) lattice field theory with electric and magnetic matter.

The complexity of temporal vertex cover in small-degree graphs (2022)
Conference Proceeding
Hamm, T., Klobas, N., Mertzios, G., & Spirakis, P. (2022). The complexity of temporal vertex cover in small-degree graphs. . https://doi.org/10.1609/aaai.v36i9.21259

Temporal graphs naturally model graphs whose underlying topology changes over time. Recently, the problems Temporal Vertex Cover (or TVC) and Sliding-Window Temporal Vertex Cover (or ∆- TVC for time-windows of a fixed-length ∆) have been established... Read More about The complexity of temporal vertex cover in small-degree graphs.

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.