A fascinating hypothesis is that human and animal intelligence could be explained by a few
principles (rather than an encyclopaedic list of heuristics). If that hypothesis was correct, we …

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …

Neural networks (NNs) are currently changing the computational paradigm on how to
combine data with mathematical laws in physics and engineering in a profound way …

One of the bottlenecks to building semiconductor chips is the increasing cost required to
develop chemical plasma processes that form the transistors and memory storage cells …

Deep ensembles have recently gained popularity in the deep learning community for their
conceptual simplicity and efficiency. However, maintaining functional diversity between …

The labels used to train machine learning (ML) models are of paramount importance.
Typically for ML classification tasks, datasets contain hard labels, yet learning using soft …

Approximate Bayesian inference for neural networks is considered a robust alternative to
standard training, often providing good performance on out-of-distribution data. However …

The solution of probabilistic inverse problems for which the corresponding forward problem
is constrained by physical principles is challenging. This is especially true if the dimension of …

Machine learning–based design has gained traction in the sciences, most notably in the
design of small molecules, materials, and proteins, with societal applications ranging from …

This century saw an unprecedented increase of public and private investments in Artificial
Intelligence (AI) and especially in (Deep) Machine Learning (ML). This led to breakthroughs …