I would like to commend Johannes Schmidt-Hieber for a very interesting and timely paper, studying nonparametric regression using deep neural networks. In recent years, the area of deep learning has seen an explosive growth within machine learning , leading to impressive leaps in performance across a wide range of important applications. However, our theoretical understanding of deep learning systems is still very limited, with many unresolved questions about their computational tractability and statistical performance. I believe that the statistics community can play a crucial role in tackling these challenging questions, and hope that Schmidt-Hieber’s paper will spur additional research. Being a computer scientist rather than a statistician, I am happy for the opportunity to provide an “outsider’s” viewpoint on this paper (of course, any opinions expressed are solely my own).

中文翻译：

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讨论：“使用具有 ReLU 激活函数的深度神经网络的非参数回归”

我要赞扬 Johannes Schmidt-Hieber 的一篇非常有趣且及时的论文，使用深度神经网络研究非参数回归。近年来，深度学习领域在机器学习中出现了爆炸性增长，导致在广泛的重要应用程序中的性能出现了令人印象深刻的飞跃。然而，我们对深度学习系统的理论理解仍然非常有限，关于它们的计算易处理性和统计性能有许多未解决的问题。我相信统计界可以在解决这些具有挑战性的问题方面发挥关键作用，并希望 Schmidt-Hieber 的论文能够激发更多的研究。作为一名计算机科学家而不是统计学家，我很高兴有机会在本文中提供“局外人”的观点（当然，