9 citations · 12 across the 6 of their papers we have counts for
5 papers · 1 filter
INDEQS: Informed Neural controlled Differential EQuationS
Michael Detzel, Gabriel Nobis, Kristiyan Blagov +4
Neural Controlled Differential Equations (NCDE) provide a powerful continuous-time framework for forecasting time series, but standard graph-based extensions typically learn spatia…
Fractional Diffusion Bridge Models
Gabriel Nobis, Maximilian Springenberg, Arina Belova +5
We present Fractional Diffusion Bridge Models (FDBM), a novel generative diffusion bridge framework driven by an approximation of the rich and non-Markovian fractional Brownian mot…
Spatial Shortcuts in Graph Neural Controlled Differential Equations
Michael Detzel, Gabriel Nobis, Jackie Ma +1
We incorporate prior graph topology information into a Neural Controlled Differential Equation (NCDE) to predict the future states of a dynamical system defined on a graph. The inf…
Generative Fractional Diffusion Models
Gabriel Nobis, Maximilian Springenberg, Marco Aversa +11
We introduce the first continuous-time score-based generative model that leverages fractional diffusion processes for its underlying dynamics. Although diffusion models have excell…
Data Models for Dataset Drift Controls in Machine Learning With Optical Images
Luis Oala, Marco Aversa, Gabriel Nobis +10
Camera images are ubiquitous in machine learning research. They also play a central role in the delivery of important services spanning medicine and environmental surveying. Howeve…