Jay Paul Morgan
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Publications
A Computability Perspective on (Verified) Machine Learning
Define the computational tasks underlying the newly suggested verified ML in a model-agnostic way, i.e., they work for all machine learning approaches including, e.g., random forests, support vector machines, and Neural Networks.
Tonicha Crook, Jay Paul Morgan, Arno Pauly, Markus Roggenbach
Domain-informed graph neural networks: a quantum chemistry case study
Research
Quantum Chemistry
Neural Network
Estimating the potential energies of out-of-equilibrium molecules and crystals using expert-informed Neural Networks.
Jay Paul Morgan, Adeline Paiement, Christian Klinke
Removing cloud shadows from ground-based solar imagery
Research
Heliophysics
Neural Network
A U-Net-style Neural Network to remove cloud contaminants from ground-based solar imagery.
Amal Chaoui, Jay Paul Morgan, Adeline Paiement, Jean Aboudarham
Adaptive Neighbourhoods for the Discovery of Adversarial Examples
Constructing unique and informed bounds for constructing adversarial examples.
Jay Paul Morgan, Adeline Paiement, Arno Pauly, Monika Seisenberger
Strategies to use prior knowledge to improve the performance of Deep Learning: an approach towards trustable Machine Learning systems
PhD Thesis of developing strategies for incorporating priors and domain knowledge into design of Deep Neural Networks.
Jay Paul Morgan
A Chatbot Framework for the Children’s Legal Centre
This paper presents a novel method to address legal rights for children through a chatbot framework by integrating machine learning, a dialogue graph, and information extraction.
Jay Paul Morgan, Adeline Paiement, Monika Seisenberger, Jane Williams, Adam Wyner
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