arXiv (All)
open access
One Size Does Not Fit All: Curvature-Aware Adaptive Perturbation for SAM
Poor generalisation in deep neural networks is frequently associated with convergence to sharp minima. Sharpness-Aware Minimisation (SAM) improves generalisation of deep neural networks by minimising a combination of the training loss and the local sharpness of the loss landscape, biasing convergenc
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
- Asynchronous Sharpness-Aware Minimization For Fast and Accurate Deep Learning#1342312
- One Size Does Not Fit All: Curvature-Aware Adaptive Perturbation for SAM#1336006
- Explaining f-Divergence-Based Regularization via Local Curvature and Sharpness-Aware Minimization#987123
- ANCRe: Adaptive Neural Connection Reassignment for Efficient Depth Scaling#1186417
- $\mathbb{SL}(n)$ Representation Learning: An Intrinsic Mixed-Curvature Space with Higher Curvature Capacities and Deeper Order-Aware Composition#1160732
Record · ID 1355339
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