Neural Networks under Epistemic Uncertainty for Robust Prediction KU Leuven
Although artificial intelligence (AI) has improved remarkably over the last few years, its inability to deal with fundamental uncertainty severely limits its application. This thesis will reimagine AI to properly treat the uncertainty stemming from our forcibly partial knowledge of the world. As currently practised, AI cannot confidently make predictions robust enough to stand the test of data generated by processes different (even by tiny ...