Use Case for Bayesian Deep Learning in the Age of ChatGPT

Many researchers have pondered the same existential questions since the release of ChatGPT: Is scale really all you need? Will the future of machine learning rely exclusively on foundation models? Should we all drop our current research agenda and work on the next large language model instead?

In this talk, MCML Senior Researcher Vincent Fortuin will try to make the case that the answer to all these questions should be a convinced "no" and that now, maybe more than ever, should be the time to focus on fundamental questions in machine learning again. He will provide evidence for this by presenting three modern use cases of Bayesian deep learning in the areas of self-supervised learning, interpretable additive modeling, and neural network sparsification. Together, these will show that the research field of Bayesian deep learning is very much alive and thriving and that its potential for valuable real-world impact is only just unfolding.