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stat.ML2025
The Computational Advantage of Depth: Learning High-Dimensional Hierarchical Functions with Gradient Descent
Yatin Dandi, Luca Pesce, Lenka Zdeborová +2
Understanding the advantages of deep neural networks trained by gradient descent (GD) compared to shallow models remains an open theoretical challenge. In this paper, we introduce…
stat.ML2025
Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions
Luca Arnaboldi, Yatin Dandi, Florent Krzakala +2
Neural networks can identify low-dimensional relevant structures within high-dimensional noisy data, yet our mathematical understanding of how they do so remains scarce. Here, we i…