Using Data-Derived Priors to Guide CNN Architecture Design for NIR Chemometrics
The study tested if measurable properties of Near-Infrared (NIR) spectral datasets could guide Convolutional Neural Network (CNN) design and hyperparameter tuning. By analyzing dataset characteristics like entropy and rank, researchers developed heuristics that suggested optimal CNN settings. These data-derived priors can help initialize shallow NIR models in plausible regions before extensive target-specific tuning.