21 citations · 46 across the 12 of their papers we have counts for
Showing 2023Show all
2 papers · 1 filter
cs.LG2023
Kernels, Data & Physics
Francesco Cagnetta, Deborah Oliveira, Mahalakshmi Sabanayagam +2
Lecture notes from the course given by Professor Julia Kempe at the summer school "Statistical physics of Machine Learning" in Les Houches. The notes discuss the so-called NTK appr…
cs.LG2023★ 21 cited
How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model
Francesco Cagnetta, Leonardo Petrini, Umberto M. Tomasini +2
Deep learning algorithms demonstrate a surprising ability to learn high-dimensional tasks from limited examples. This is commonly attributed to the depth of neural networks, enabli…