Near-optimal estimates for the p-Lipschitz constants of deep random ReLU neural networks

arXiv:2506.19695 · stat.ML, cs.LG, math.PR · Submitted 2025-06-24 · Read on arXiv

stat.ML, cs.LG, math.PR

Submitted: 2025-06-24

Updated: 2026-09-10

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Generalization — 4× in this paper · explained in 13 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Adversarial Examples — 1× in this paper · explained in 2 episodes
• Adversarial Robustness — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• IEEE International Conference on Computer Vision — 1× in this paper
• Sparsity — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• A Convergence Theory for Deep Learning via Over-Parameterization
• Near-Optimal Bounds for Binary Embeddings of Arbitrary Sets

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