Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model

Documenti analoghi

• A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities
  di: Dandi, Yatin, et al.
  Pubblicazione: (2024)
• Universality laws for Gaussian mixtures in generalized linear models
  di: Dandi, Yatin, et al.
  Pubblicazione: (2023)
• Deep Learning of Compositional Targets with Hierarchical Spectral Methods
  di: Tabanelli, Hugo, et al.
  Pubblicazione: (2026)
• Deep Learning as Neural Low-Degree Filtering: A Spectral Theory of Hierarchical Feature Learning
  di: Dandi, Yatin, et al.
  Pubblicazione: (2026)
• Gaussian Universality of Perceptrons with Random Labels
  di: Gerace, Federica, et al.
  Pubblicazione: (2022)