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cs.LG2026
GraphNNK -- Graph Classification and Interpretability
Zeljko Bolevic, Milos Brajovic, Isidora Stankovic +1
Graph Neural Networks (GNNs) have become a standard approach for learning from graph-structured data. However, their reliance on parametric classifiers (most often linear softmax l…
cs.LG2024
Widely Linear Matched Filter: A Lynchpin towards the Interpretability of Complex-valued CNNs
Qingchen Wang, Zhe Li, Zdenka Babic +3
A recent study on the interpretability of real-valued convolutional neural networks (CNNs) {Stankovic_Mandic_2023CNN} has revealed a direct and physically meaningful link with the…