743 citations · 1.4k across the 18 of their papers we have counts for
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A jamming transition from under- to over-parametrization affects loss landscape and generalization
Stefano Spigler, Mario Geiger, Stéphane d'Ascoli +3
We argue that in fully-connected networks a phase transition delimits the over- and under-parametrized regimes where fitting can or cannot be achieved. Under some general condition…
The jamming transition as a paradigm to understand the loss landscape of deep neural networks
Mario Geiger, Stefano Spigler, Stéphane d'Ascoli +4
Deep learning has been immensely successful at a variety of tasks, ranging from classification to AI. Learning corresponds to fitting training data, which is implemented by descend…
Electromagnetic Emission from Supermassive Binary Black Holes Approaching Merger
Stéphane d'Ascoli, Scott C. Noble, Dennis B. Bowen +3
We present the first fully relativistic prediction of the electromagnetic emission from the surrounding gas of a supermassive binary black hole system approaching merger. Using a r…