Generative Fractional Diffusion Models

no code implementations • 26 Oct 2023 • Gabriel Nobis, Marco Aversa, Maximilian Springenberg, Michael Detzel, Stefano Ermon, Shinichi Nakajima, Roderick Murray-Smith, Sebastian Lapuschkin, Christoph Knochenhauer, Luis Oala, Wojciech Samek

We generalize the continuous time framework for score-based generative models from an underlying Brownian motion (BM) to an approximation of fractional Brownian motion (FBM).

Data Models for Dataset Drift Controls in Machine Learning With Optical Images

1 code implementation • 4 Nov 2022 • Luis Oala, Marco Aversa, Gabriel Nobis, Kurt Willis, Yoan Neuenschwander, Michèle Buck, Christian Matek, Jerome Extermann, Enrico Pomarico, Wojciech Samek, Roderick Murray-Smith, Christoph Clausen, Bruno Sanguinetti

This limits our ability to study and understand the relationship between data generation and downstream machine learning model performance in a physically accurate manner.

Model Selection

Bessel Equivariant Networks for Inversion of Transmission Effects in Multi-Mode Optical Fibres

no code implementations • 26 Jul 2022 • Joshua Mitton, Simon Peter Mekhail, Miles Padgett, Daniele Faccio, Marco Aversa, Roderick Murray-Smith

We develop a new type of model for solving the task of inverting the transmission effects of multi-mode optical fibres through the construction of an $\mathrm{SO}^{+}(2, 1)$-equivariant neural network.