Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David Dunson, Maurizio Filippone, Vincent Fortuin
Abstract
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.
BibTeX
@inproceedings{
papamarkou2024position,
title={Position: Bayesian Deep Learning is Needed in the Age of Large-Scale {AI}},
author={Theodore Papamarkou and Maria Skoularidou and Konstantina Palla and Laurence Aitchison and Julyan Arbel and David Dunson and Maurizio Filippone and Vincent Fortuin and Philipp Hennig and Jos{\'e} Miguel Hern{\'a}ndez-Lobato and Aliaksandr Hubin and Alexander Immer and Theofanis Karaletsos and Mohammad Emtiyaz Khan and Agustinus Kristiadi and Yingzhen Li and Stephan Mandt and Christopher Nemeth and Michael A Osborne and Tim G. J. Rudner and David R{\"u}gamer and Yee Whye Teh and Max Welling and Andrew Gordon Wilson and Ruqi Zhang},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=PrmxFWI1Fr}
}