133 citations · 188 across the 11 of their papers we have counts for
6 papers · 1 filter
Beyond adaptive gradient: Fast-Controlled Minibatch Algorithm for large-scale optimization
Corrado Coppola, Lorenzo Papa, Irene Amerini +1
Adaptive gradient methods have been increasingly adopted by deep learning community due to their fast convergence and reduced sensitivity to hyper-parameters. However, these method…
On the impact of key design aspects in simulated Hybrid Quantum Neural Networks for Earth Observation
Lorenzo Papa, Alessandro Sebastianelli, Gabriele Meoni +1
Quantum computing has introduced novel perspectives for tackling and improving machine learning tasks. Moreover, the integration of quantum technologies together with well-known de…
Computational issues in Optimization for Deep networks
Corrado Coppola, Lorenzo Papa, Marco Boresta +2
The paper aims to investigate relevant computational issues of deep neural network architectures with an eye to the interaction between the optimization algorithm and the classific…
METER: a mobile vision transformer architecture for monocular depth estimation
L. Papa, P. Russo, I. Amerini
Depth estimation is a fundamental knowledge for autonomous systems that need to assess their own state and perceive the surrounding environment. Deep learning algorithms for depth…
D4D: An RGBD diffusion model to boost monocular depth estimation
L. Papa, P. Russo, I. Amerini
Ground-truth RGBD data are fundamental for a wide range of computer vision applications; however, those labeled samples are difficult to collect and time-consuming to produce. A co…
Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It
Federico Siciliano, Luca Maiano, Lorenzo Papa +3
Fake news detection models are critical to countering disinformation but can be manipulated through adversarial attacks. In this position paper, we analyze how an attacker can comp…