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