4 papers
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…
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…
RDD: Pareto Analysis of the Rate-Distortion-Distinguishability Trade-off
Andriy Enttsel, Alex Marchioni, Andrea Zanellini +3
Extensive monitoring systems generate data that is usually compressed for network transmission. This compressed data might then be processed in the cloud for tasks such as anomaly…
Robust Load Disturbance Rejection in PWM DC-DC Buck Converters
Simone Pirrera, Francesco Gabriele, Davide Lena +3
This paper presents a novel approach to robust load disturbance rejection in DC-DC Buck converters. We propose a novel control scheme based on the design of two nested feedback loo…