Mathematical analysis shows sparse linear regression mitigates output dimension collapse in brain-to-image reconstruction at small data scales by exploiting sparsity in the brain-to-feature mapping.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
CCNETS is a new modular causal framework using three cooperative modules and a Zoint mechanism to align synthetic data generation with classifier needs on imbalanced pattern recognition tasks.
citing papers explorer
-
Overcoming Output Dimension Collapse: When Sparsity Enables Zero-shot Brain-to-Image Reconstruction at Small Data Scales
Mathematical analysis shows sparse linear regression mitigates output dimension collapse in brain-to-image reconstruction at small data scales by exploiting sparsity in the brain-to-feature mapping.
-
CCNETS: A Modular Causal Learning Framework for Pattern Recognition in Imbalanced Datasets
CCNETS is a new modular causal framework using three cooperative modules and a Zoint mechanism to align synthetic data generation with classifier needs on imbalanced pattern recognition tasks.