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.

翻译：在当前的深度学习研究格局中，主要侧重于在涉及大型图像和语言数据集的监督任务中实现高预测精度。然而，更广阔的视角揭示出大量被忽视的指标、任务和数据类型，如不确定性、主动学习、持续学习以及科学数据等，这些都需要得到关注。贝叶斯深度学习（BDL）是一条有前景的途径，在这些多元化场景中展现出优势。本文认为BDL能够提升深度学习的能力。它回顾了BDL的优势，承认了现有的挑战，并重点介绍了一些旨在攻克这些障碍的令人兴奋的研究方向。展望未来，讨论聚焦于将大规模基础模型与BDL相结合以释放其全部潜力的可能方法。