arXiv (All)
open access
Gate-Based Feature Importance in Simulation-Based Inference for EEG: A Cautionary Tale
Simulation-based inference (SBI) provides a powerful way to fit mechanistic models to data, but offers little insight into which information in the data shapes the inferred parameter values. It could therefore benefit from robust and inexpensive interpretability methods. Learned weighting mechanisms
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
- ThinkNet: Compact Architecture Selection and Validation-Gated Ensembles for Subject-Independent MI-EEG Decoding#1119040
- Stress-Testing EEG Foundation Models for Clinical Decoding: Dataset Identity and Targeted Negative Controls#405661
- Electrophysiological signatures of statistical segmentation: A systematic review and meta-analysis#976775
- Electrophysiological signatures of statistical segmentation: A systematic review and meta-analysis#976669
- Behavioral Latency as Weak Event-Time Supervision for EEG Reaction-Time Decoding#794218
Record · ID 1423156
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