Home | Events

28

Feb

Teaser image to Use Case for Bayesian Deep Learning in the age of ChatGPT

 

Use Case for Bayesian Deep Learning in the age of ChatGPT

Vincent Fortuin, Helmholtz AI & MCML

   28.02.2024

   4:15 pm - 5:45 pm

   LMU Department of Statistics and via zoom

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.

Related

Link to Privacy, Data Privacy, and Differential Privacy

 

Colloquium  •  16.07.2024  •  LMU Department of Statistics and via zoom

Privacy, Data Privacy, and Differential Privacy

Colloquium at the LMU Department of Statistics with James Bailie from Harvard University.

Link to Variational Learning for Large Deep Networks

 

Colloquium  •  10.07.2024  •  LMU Department of Statistics and via zoom

Variational Learning for Large Deep Networks

Colloquium at the LMU Department of Statistics with Thomas Möllenhoff from RIKEN, Tokyo.

Link to Can today’s intention to treat have a causal effect on tomorrow’s hazard function?

 

Colloquium  •  03.07.2024  •  LMU Department of Statistics and via zoom

Can today’s intention to treat have a causal effect on tomorrow’s hazard function?

Colloquium at the LMU Department of Statistics with Jan Beyersmann, University of Ulm.

Link to The Complexities of Differential Privacy for Survey Data

 

Colloquium  •  26.06.2024  •  LMU Department of Statistics and via zoom

The Complexities of Differential Privacy for Survey Data

Link to Resampling-based inference for the average treatment effect in observational studies with competing risks

 

Colloquium  •  19.06.2024  •  LMU Department of Statistics and via zoom

Resampling-based inference for the average treatment effect in observational studies with competing risks

This talk explores three resampling methods to construct valid confidence intervals and bands for treatment effect estimation in competing risks studies.