4 papers
Deep Generative Classification of Blood Cell Morphology
Simon Deltadahl, Julian Gilbey, Christine Van Laer +16
Accurate classification of haematological cells is critical for diagnosing blood disorders, but presents significant challenges for machine automation owing to the complexity of ce…
A physics-informed neural network framework for modeling obstacle-related equations
Hamid El Bahja, Jan Christian Hauffen, Peter Jung +2
Deep learning has been highly successful in some applications. Nevertheless, its use for solving partial differential equations (PDEs) has only been of recent interest with current…
Physics-informed neural networks for Timoshenko system with Thermoelasticity
Sabrine Chebbi, Joseph Muthui Wacira, Makram Hamouda +1
The main focus of this paper is to analyze the behavior of a numerical solution of the Timoshenko system coupled with Thermoelasticity and incorporating second sound effects. In or…
This actually looks like that: Proto-BagNets for local and global interpretability-by-design
Kerol Djoumessi, Bubacarr Bah, Laura Kühlewein +2
Interpretability is a key requirement for the use of machine learning models in high-stakes applications, including medical diagnosis. Explaining black-box models mostly relies on…