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cs.AI2025
Explainability-Driven Dimensionality Reduction for Hyperspectral Imaging
Salma Haidar, José Oramas
Hyperspectral imaging (HSI) provides rich spectral information for precise material classification and analysis; however, its high dimensionality introduces a computational burden…
cs.LG2025
Compositionality Unlocks Deep Interpretable Models
Thomas Dooms, Ward Gauderis, Geraint A. Wiggins +1
We propose -net, an intrinsically interpretable architecture combining the compositional multilinear structure of tensor networks with the expressivity and efficiency of deep ne…
cs.LG2025
Efficient Post-Hoc Uncertainty Calibration via Variance-Based Smoothing
Fabian Denoodt, José Oramas
Since state-of-the-art uncertainty estimation methods are often computationally demanding, we investigate whether incorporating prior information can improve uncertainty estimates…