22 citations · 60 across the 19 of their papers we have counts for
23 papers
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…
Improved identification accuracy in equation learning via comprehensive -elimination and Bayesian model selection
Daniel Nickelsen, Bubacarr Bah
In the field of equation learning, exhaustively considering all possible equations derived from a basis function dictionary is infeasible. Sparse regression and greedy algorithms h…
Recent Methodological Advances in Federated Learning for Healthcare
Fan Zhang, Daniel Kreuter, Yichen Chen +10
For healthcare datasets, it is often not possible to combine data samples from multiple sites due to ethical, privacy or logistical concerns. Federated learning allows for the util…
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…
Efficient and Robust Mixed-Integer Optimization Methods for Training Binarized Deep Neural Networks
Jannis Kurtz, Bubacarr Bah
Compared to classical deep neural networks its binarized versions can be useful for applications on resource-limited devices due to their reduction in memory consumption and comput…