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; Philipp Hennig; José Miguel Hernández-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 Rügamer; Yee Whye Teh; Max Welling; Andrew Gordon Wilson; Ruqi Zhang

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, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G.J. RudnerDavid Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang

^*Corresponding author for this work

Research output: Contribution to conference › Paper › peer-review

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.

Original language	English (US)
Pages	39556-39586
Number of pages	31
State	Published - 2024
Event	41st International Conference on Machine Learning, ICML 2024 - Vienna, Austria Duration: Jul 21 2024 → Jul 27 2024

Conference

Conference	41st International Conference on Machine Learning, ICML 2024
Country/Territory	Austria
City	Vienna
Period	07/21/24 → 07/27/24

ASJC Scopus subject areas

Artificial Intelligence
Software
Control and Systems Engineering
Statistics and Probability

Cite this

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. 39556-39586. Paper presented at 41st International Conference on Machine Learning, ICML 2024, Vienna, Austria.

@conference{62e8dc95cf2d41149bd2fdc730c63a35,

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.",

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",

year = "2024",

language = "English (US)",

pages = "39556--39586",

}

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', Paper presented at 41st International Conference on Machine Learning, ICML 2024, Vienna, Austria, 07/21/24 - 07/27/24 pp. 39556-39586.

TY - CONF

T1 - Position

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

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

PY - 2024

Y1 - 2024

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.

UR - http://www.scopus.com/inward/record.url?scp=85203823735&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:85203823735

SP - 39556

EP - 39586

Y2 - 21 July 2024 through 27 July 2024

ER -

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

Abstract

Conference

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this