Seminars

Ferdinand Mom is a Research Engineer at Hugging Face with a background in large-scale pretraining and efficient deep learning systems.

Do Large Language Models (LLMs) think and reason? Are they perpetual information machines, producing endless coherent and correct text from finite training data? We explore how LLMs work and whether they produce rational thought and endless information.

This seminar examines the mechanisms of catastrophic forgetting in large-scale AI systems, with particular emphasis on applications in neuroscience. We explore how continual learning on real-world data can lead to knowledge degradation, where sequential training progressively erodes previously acquired representations.

Algorithmic information theory has a wide range of applications, despite the fact that its core quantity, Kolmogorov complexity, is incomputable.

Gaussian processes (GPs) are often considered to be the gold standard in settings where well-calibrated predictive uncertainty is of key importance, such as decision making.

Synthetic theories such as homotopy type theory axiomatize classical mathematical objects such as spaces up to homotopy. Although theorems in synthetic theories translate to theorems about the axiomatized structures on paper, this fact has not yet been exploited in proof assistants.

Abstract not available

Abstract not available

*Abstract*