arXiv AI By Paul Caillon, Christophe Cerisara, Alexandre Allauzen

Across the Loss Landscape with Progressive Growth

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arXiv Machine Learning
Jul 1

Revisiting the Volume Hypothesis

arXiv:2606. 31282v1 Announce Type: new Abstract: Modern deep neural networks often contain far more parameters than needed to fit their training data, yet they achieve impressive generalization.

By Ari Pakman, Lior Kreimer, Yakir Berchenko
arXiv Machine Learning
Jun 30

Why Do We Need Warm-up? A Theoretical Perspective

arXiv:2510. 03164v2 Announce Type: replace Abstract: Learning rate warm-up -- increasing the learning rate at the beginning of training -- has become a ubiquitous heuristic in modern deep learning, yet its theoretical foundations remain poorly understood.

By Foivos Alimisis, Rustem Islamov, Aurelien Lucchi