arXiv (All)
open access
Annealed correlation and quenched noise sensitivity in biased deep neural networks
In earlier work by Jonasson, Steif and Zetterqvist (2023), fully connected deep neural networks with layers of width $n$, i.i.d.\ standard Gaussian weights, sign activations and no bias terms were shown to be annealed and quenched noise sensitive with respect to any joint probability distribution $\
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Residual-Guided Randomized Neural Networks#618571
- Weakly nonlinear response of noisy neurons#641964
- Robustness of neural networks to random noise perturbations of their inputs#324917
- Dual Randomized Smoothing: Beyond Global Noise Variance#1122974
- Theoretical Lower Bounds on the Robustness of Deep ReLU Networks#1187320
Record · ID 1411958
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive