Research map / Probabilistic and scientific modeling / Dynamical systems
Learning Interpretable Models for Coupled Networks Under Domain Constraints
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply increasingly informative hard network constraints (ablation) | CC-MRCE · Simulated data | Reconstruction MSE percentage (100%): 50.88 -> 43.24 -> 43.18 -> 42.98 | Helped |
| Apply increasingly informative hard network constraints (ablation) | CC-MRCE · Simulated data | Pearson's r-score: 0.709 -> 0.763 -> 0.764 -> 0.764 | Helped |
| Use non-Gaussian pseudolikelihood modeling rather than Gaussian MRCE | CC-MRCE · Simulated data | Reconstruction MSE percentage (100%): 50.88 vs 60.22 | Helped |
| Use non-Gaussian pseudolikelihood modeling rather than Gaussian CGGM | CC-MRCE · Simulated data | Reconstruction MSE percentage (100%): 50.88 vs 76.21 | Helped |
| Increase VAE layer count or latent dimensions (ablation) | VAE · HCP task FC reconstruction | final performance | No clear effect |
| Use CC-MRCE instead of the strongest reported baseline, Spectral Mapping | HCP task FC reconstruction · Seven HCP tasks | Reconstruction MSE percentage (100%) | Helped |
| Use structured sparsity in the learned coupling instead of random coefficients with the same sparsity level | CC-MRCE · HCP task FC reconstruction | Pearson's r-score | Helped |
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