Depth Separations in Neural Networks: Separating the Dimension from the Accuracy

Documenti analoghi

• The Median is Easier than it Looks: Approximation with a Constant-Depth, Linear-Width ReLU Network
  di: Dutta, Abhigyan, et al.
  Pubblicazione: (2026)
• A Depth Hierarchy for Computing the Maximum in ReLU Networks via Extremal Graph Theory
  di: Safran, Itay
  Pubblicazione: (2026)
• Provable Privacy Attacks on Trained Shallow Neural Networks
  di: Smorodinsky, Guy, et al.
  Pubblicazione: (2024)
• On the Rate of Convergence of GD in Non-linear Neural Networks: An Adversarial Robustness Perspective
  di: Smorodinsky, Guy, et al.
  Pubblicazione: (2026)
• No Prior, No Leakage: Revisiting Reconstruction Attacks in Trained Neural Networks
  di: Refael, Yehonatan, et al.
  Pubblicazione: (2025)