arXiv (All)
open access
AERIAL: Adversarial Evaluation of Robustness in Accuracy-Preserving Low-Precision EEG Decoders
Deployment-oriented compression is attractive for resource-constrained brain--computer interfaces (BCIs), but whether it changes adversarial vulnerability remains unclear. On BCI Competition IV-2a, we compare 32-bit floating-point (FP32) EEGNet and ShallowConvNet models with global magnitude pruning
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
- CLAD: Constrained Abstract Domain for Neural Network Verification#1128890
- CLAD: Constrained Abstract Domain for Neural Network Verification#1108833
- Counter-GEO-Bench: Evaluating Defenses Against Information-Distorting Generative Engine Optimization#924212
- Counter-GEO-Bench: Evaluating Defenses Against Information-Distorting Generative Engine Optimization#919981
- Revisiting Code Debloating with Ground Truth-based Evaluation#120588
Record · ID 1078108
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