Tuesday, November 7, 2023
09:00 - 13:00
In the recent years the interest has increased for neural models that do not learn simply from correlations but take into account, learn from, and try to infer cause-and-effect mechanisms. More generally, imbuing prior knowledge regarding the underlying structures of the solution space has been a core question for deep learning, generative AI, trustworthy neural networks, and so on. In this workshop, we have the pleasure to host foremost experts in causal and structured deep learning, including Francesco Locatello (ISTA, Austria), Jakub Tomczak (TUe), Sara Magliacane (UvA), to share their recent findings with us
De Polder
Symposium
complexity, computational physics, emergence, mathematical physics