Feb. 2, 2024, 9:46 p.m. | Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel David Dunson M

cs.LG updates on arXiv.org arxiv.org

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. …

accuracy age bayesian bayesian deep learning continual cs.lg current data datasets deep learning image landscape language metrics paper perspective predictive research scale scale ai stat.ml tasks types uncertainty

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