Are you passionate about cutting-edge research on multimodal reasoning, operating in an international research environment, and co-supervising PhD students? Then keep reading: this 2-year fully-funded...

While deep ensembles are widely considered to be the default method for uncertainty quantification in deep learning, their effectiveness for graph-structured data is often simply assumed based on succ...

<p><span>Are you passionate about advancing Machine Learning by integrating insights from the natural sciences? Are you eager to bridge the 3rd (<em><span>computational</span></em>) and 4th (<em><span...

Transformer architectures cannot simulate computer programs. They are not Turing-complete, despite what several papers have claimed.

Are you interested in improving the interpretability, robustness and safety of AI by integrating causal reasoning? The Causality team in the AMLab group at the University of Amsterdam is looking for 2...

We give an algorithm that learns arbitrary Boolean functions of $k$ arbitrary halfspaces over $\mathbb{R}^n$, in the challenging distribution-free Probably Approximately Correct (PAC) learning model, ...

A large driver of the complexity of graph learning is the interplay between structure and features. When analyzing the expressivity of graph neural networks, however, existing approaches ignore featur...