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

Research output: Contribution to Journal/Magazine › Conference article › peer-review

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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. / Papamarkou, Theodore; Skoularidou, Maria; Palla, Konstantina et al.
In: Proceedings of Machine Learning Research, Vol. 235, 31.12.2024, p. 39556-39586.

Research output: Contribution to Journal/Magazine › Conference article › peer-review

Harvard

Papamarkou, T, Skoularidou, M, Palla, K, Aitchison, L, Arbel, J, Dunson, D, Filippone, M, Fortuin, V, Hennig, P, Hernández-Lobato, JM, Hubin, A, Immer, A, Karaletsos, T, Khan, ME, Kristiadi, A, Li, Y, Mandt, S, Nemeth, C, Osborne, MA, Rudner, TGJ, Rügamer, D, Teh, YW, Welling, M, Wilson, AG & Zhang, R 2024, 'Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI', Proceedings of Machine Learning Research, vol. 235, pp. 39556-39586. <https://proceedings.mlr.press/v235/papamarkou24b.html>

APA

Papamarkou, T., Skoularidou, M., Palla, K., Aitchison, L., Arbel, J., Dunson, D., Filippone, M., Fortuin, V., Hennig, P., Hernández-Lobato, J. M., Hubin, A., Immer, A., Karaletsos, T., Khan, M. E., Kristiadi, A., Li, Y., Mandt, S., Nemeth, C., Osborne, M. A., ... Zhang, R. (2024). Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. Proceedings of Machine Learning Research, 235, 39556-39586. https://proceedings.mlr.press/v235/papamarkou24b.html

Vancouver

Papamarkou T, Skoularidou M, Palla K, Aitchison L, Arbel J, Dunson D et al. Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. Proceedings of Machine Learning Research. 2024 Dec 31;235:39556-39586.

Author

Papamarkou, Theodore ; Skoularidou, Maria ; Palla, Konstantina et al. / Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. In: Proceedings of Machine Learning Research. 2024 ; Vol. 235. pp. 39556-39586.

Bibtex

@article{f58542a500f44e96a6b35d8f5922ecb7,
title = "Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI",
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.",
keywords = "cs.LG, stat.ML",
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 Hern{\'a}ndez-Lobato, {Jos{\'e} Miguel} and Aliaksandr Hubin and Alexander Immer and Theofanis Karaletsos and Khan, {Mohammad Emtiyaz} and Agustinus Kristiadi and Yingzhen Li and Stephan Mandt and Christopher Nemeth and Osborne, {Michael A.} and Rudner, {Tim G. J.} and David R{\"u}gamer and Teh, {Yee Whye} and Max Welling and Wilson, {Andrew Gordon} and Ruqi Zhang",
note = "In: Proceedings of the 41st International Conference on Machine Learning (ICML), Vienna, Austria. ; 41st International Conference on Machine Learning, ICML 2024 ; Conference date: 21-07-2024 Through 27-07-2024",
year = "2024",
month = dec,
day = "31",
language = "English",
volume = "235",
pages = "39556--39586",
journal = "Proceedings of Machine Learning Research",
issn = "1938-7228",
publisher = "ML Research Press",

}

RIS

TY - JOUR

T1 - Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

AU - Papamarkou, Theodore

AU - Skoularidou, Maria

AU - Palla, Konstantina

AU - Aitchison, Laurence

AU - Arbel, Julyan

AU - Dunson, David

AU - Filippone, Maurizio

AU - Fortuin, Vincent

AU - Hennig, Philipp

AU - Hernández-Lobato, José Miguel

AU - Hubin, Aliaksandr

AU - Immer, Alexander

AU - Karaletsos, Theofanis

AU - Khan, Mohammad Emtiyaz

AU - Kristiadi, Agustinus

AU - Li, Yingzhen

AU - Mandt, Stephan

AU - Nemeth, Christopher

AU - Osborne, Michael A.

AU - Rudner, Tim G. J.

AU - Rügamer, David

AU - Teh, Yee Whye

AU - Welling, Max

AU - Wilson, Andrew Gordon

AU - Zhang, Ruqi

N1 - In: Proceedings of the 41st International Conference on Machine Learning (ICML), Vienna, Austria.

PY - 2024/12/31

Y1 - 2024/12/31

N2 - 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.

AB - 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.

KW - cs.LG

KW - stat.ML

M3 - Conference article

VL - 235

SP - 39556

EP - 39586

JO - Proceedings of Machine Learning Research

JF - Proceedings of Machine Learning Research

SN - 1938-7228

T2 - 41st International Conference on Machine Learning, ICML 2024

Y2 - 21 July 2024 through 27 July 2024

ER -