1.4k citations · 4.6k across the 52 of their papers we have counts for
Showing 2023 · cs.LGShow all
2 papers · 2 filters
cs.LG2023★ 2 cited
Minimizing Energy Consumption of Deep Learning Models by Energy-Aware Training
Dario Lazzaro, Antonio Emanuele Cinà, Maura Pintor +4
Deep learning models undergo a significant increase in the number of parameters they possess, leading to the execution of a larger number of operations during inference. This expan…
cs.LG2023★ 1 cited
Dropout Injection at Test Time for Post Hoc Uncertainty Quantification in Neural Networks
Emanuele Ledda, Giorgio Fumera, Fabio Roli
Among Bayesian methods, Monte-Carlo dropout provides principled tools for evaluating the epistemic uncertainty of neural networks. Its popularity recently led to seminal works that…