52 citations · 80 across the 14 of their papers we have counts for
4 papers · 1 filter
Domain-informed graph neural networks: a quantum chemistry case study
Jay Morgan, Adeline Paiement, Christian Klinke
We explore different strategies to integrate prior domain knowledge into the design of a deep neural network (DNN). We focus on graph neural networks (GNN), with a use case of esti…
Detecting Humans in RGB-D Data with CNNs
Kaiyang Zhou, Adeline Paiement, Majid Mirmehdi
We address the problem of people detection in RGB-D data where we leverage depth information to develop a region-of-interest (ROI) selection method that provides proposals to two c…
A DNN Framework for Learning Lagrangian Drift With Uncertainty
Joseph Jenkins, Adeline Paiement, Yann Ourmières +4
Reconstructions of Lagrangian drift, for example for objects lost at sea, are often uncertain due to unresolved physical phenomena within the data. Uncertainty is usually overcome…
Characterization of Low Surface Brightness structures in annotated deep images
Elisabeth Sola, Pierre-Alain Duc, Felix Richards +7
The characterization of Low Surface Brightness (LSB) stellar structures around galaxies such as tidal debris of on-going or past collisions is essential to constrain models of gala…