11th International Conference on Scale Space and Variational Methods in Computer Vision, held in 2027 in Veldhoven, the Netherlands. The conference serves as a primary forum for researchers at the int...

<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...

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...

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...

While widespread, Transformers lack inductive biases for geometric symmetries common in science and computer vision. Existing equivariant methods often sacrifice the efficiency and flexibility that ma...

Are you interested in improving the interpretability, robustness and safety of current AI systems? If the answer is yes, please continue reading!

The success of modern AI systems has been largely driven by massive scaling of data and compute resources. However, in scientific applications, where

Multiple (pre-)clinical trials are required in the regulatory trajectory to introduce new medical devices in clinical practice. The process is widely acknowledged as sub-optimal, resource-heavy, and t...

The Latent Stochastic Differential Equation (SDE) is a powerful tool for time series and sequence modeling. However, training Latent SDEs typically relies on adjoint sensitivity methods, which depend ...

Accurate prediction of RNA properties, such as stability and interactions, is crucial for advancing our understanding of biological processes and developing RNA-based therapeutics. RNA structures can ...

Conditional Neural Fields (CNFs) are increasingly being leveraged as continuous signal representations, by associating each data-sample with a latent variable that conditions a shared backbone...

Part 1 (Rita Cucchiara): Foundation Models, pretrained on extremely large unknown source of data, contain in their embed space many information

Group equivariance has emerged as a valuable inductive bias in deep learning, enhancing generalization, data efficiency, and robustness. Classically, group equivariant methods require the groups of in...