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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

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arxiv 2402.00809 v5 pith:C2HFGXCX submitted 2024-02-01 cs.LG stat.ML

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
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Philipp Hennig Jos\'e Miguel Hern\'andez-Lobato Aliaksandr Hubin Alexander Immer Theofanis Karaletsos Mohammad Emtiyaz Khan Agustinus Kristiadi Yingzhen Li Stephan Mandt Christopher Nemeth Michael A. Osborne Tim G. J. Rudner David R\"ugamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang
This is my paper
classification cs.LG stat.ML
keywords learningdeepbayesiandatalarge-scaleresearchtasksaccuracy
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 4 Pith papers

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    A unified taxonomy of uncertainty in ML for physics is introduced together with validation tools such as coverage, calibration, and proper scoring rules, illustrated on regression and classification tasks.

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    A community-review taxonomy of uncertainty in physics-ML, plus validation diagnostics and toy experiments showing that no tested UQ method keeps nominal coverage outside the training domain.