Disentanglement
Cross-source consensus on Disentanglement from 1 sources and 5 claims.
1 sources · 5 claims
How it works
Benefits
Evidence quality
Highlighted claims
- Adding the TCR penalty is interpreted as making tokens less redundant and more specialized. — Learning Fingerprints for Medical Time Series with Redundancy-Constrained Information Maximization
- The synthetic perturbation experiment indicated that tokens specialized in different local motifs without explicit supervision. — Learning Fingerprints for Medical Time Series with Redundancy-Constrained Information Maximization
- The diversity objective is reported to encourage independent local anomalies to be isolated rather than distributed everywhere. — Learning Fingerprints for Medical Time Series with Redundancy-Constrained Information Maximization
- EEG attention was broader and multi-slot, consistent with heterogeneous cortical dynamics. — Learning Fingerprints for Medical Time Series with Redundancy-Constrained Information Maximization
- ECG attention concentrated in fewer slots, suggesting compact morphological cues dominate. — Learning Fingerprints for Medical Time Series with Redundancy-Constrained Information Maximization