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20192023
most citedenpheeph: A Fault Injection Framework for Spiking and Compressed Deep Neural Networks

18 citations · 20 across the 4 of their papers we have counts for

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5 papers · 1 filter

cs.LG2023

RobCaps: Evaluating the Robustness of Capsule Networks against Affine Transformations and Adversarial Attacks

Alberto Marchisio, Antonio De Marco, Alessio Colucci +2

Capsule Networks (CapsNets) are able to hierarchically preserve the pose relationships between multiple objects for image classification tasks. Other than achieving high accuracy,…

cs.LG2023★ 2 cited

ISimDL: Importance Sampling-Driven Acceleration of Fault Injection Simulations for Evaluating the Robustness of Deep Learning

Alessio Colucci, Andreas Steininger, Muhammad Shafique

Deep Learning (DL) systems have proliferated in many applications, requiring specialized hardware accelerators and chips. In the nano-era, devices have become increasingly more sus…

cs.LG2020

MLComp: A Methodology for Machine Learning-based Performance Estimation and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences

Alessio Colucci, Dávid Juhász, Martin Mosbeck +6

Embedded systems have proliferated in various consumer and industrial applications with the evolution of Cyber-Physical Systems and the Internet of Things. These systems are subjec…

cs.LG2020

Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks

Alberto Marchisio, Beatrice Bussolino, Alessio Colucci +3

Capsule Networks (CapsNets), recently proposed by the Google Brain team, have superior learning capabilities in machine learning tasks, like image classification, compared to the t…

cs.LG2019

FasTrCaps: An Integrated Framework for Fast yet Accurate Training of Capsule Networks

Alberto Marchisio, Beatrice Bussolino, Alessio Colucci +4

Recently, Capsule Networks (CapsNets) have shown improved performance compared to the traditional Convolutional Neural Networks (CNNs), by encoding and preserving spatial relations…