High Probability Derivative Bounds for Random tanh Neural Networks on a Hypercube
cs.LG, cs.NA, math.NA
Submitted: 2026-08-27
Updated: 2026-08-27
Terminology
Sources
- Large-width functional asymptotics for deep Gaussian neural networks
- Near-optimal estimates for the p-Lipschitz constants of deep random ReLU neural networks
- Explaining and Harnessing Adversarial Examples
- Deep Neural Networks as Gaussian Processes
- Gaussian Process Behaviour in Wide Deep Neural Networks
- Provable Bounds on the Hessian of Neural Networks: Derivative-Preserving Reachability Analysis
- Intriguing properties of neural networks
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks