Hub-LoRA
Cross-source consensus on Hub-LoRA from 1 sources and 5 claims.
1 sources · 5 claims
Uses
How it works
Benefits
Highlighted claims
- Hub-LoRA applies low-rank adaptation to the first layer so adaptation directly interacts with input features. — Foundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity
- Hub-LoRA constrains adaptation toward hub nodes by freezing a one-hot matrix initialized from nodes whose ambivert degree exceeds 1.0. — Foundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity
- Hub-LoRA was proposed because standard foundation-model fine-tuning did not sufficiently capture hub structures. — Foundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity
- Hub-LoRA improved Brain-JEPA's biomarker metrics while maintaining other metrics. — Foundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity
- Hub-LoRA improved hub sensitivity but its accuracy gains were not statistically significant. — Foundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity