47 citations · 110 across the 27 of their papers we have counts for
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cs.LG2022★ 2 cited
Fault-Aware Design and Training to Enhance DNNs Reliability with Zero-Overhead
Niccolò Cavagnero, Fernando Dos Santos, Marco Ciccone +3
Deep Neural Networks (DNNs) enable a wide series of technological advancements, ranging from clinical imaging, to predictive industrial maintenance and autonomous driving. However,…
cs.LG2017★ 7 cited
Training Deep Networks without Learning Rates Through Coin Betting
Francesco Orabona, Tatiana Tommasi
Deep learning methods achieve state-of-the-art performance in many application scenarios. Yet, these methods require a significant amount of hyperparameters tuning in order to achi…