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cs.LG2026
Vanishing Contributions: A Unified Framework for Smooth and Iterative Model Compression
Lorenzo Nikiforos, Luciano Prono, Charalampos Antoniadis +3
The increasing scale of Deep Neural Networks (DNNs) introduces the need for compression techniques such as pruning, quantization, and low-rank decomposition. While these methods ar…
cs.LG2025
Multi-Layer Confidence Scoring for Detection of Out-of-Distribution Samples, Adversarial Attacks, and In-Distribution Misclassifications
Lorenzo Capelli, Leandro de Souza Rosa, Gianluca Setti +2
The recent explosive growth in Deep Neural Networks applications raises concerns about the black-box usage of such models, with limited trasparency and trustworthiness in high-stak…