5 citations · 8 across the 8 of their papers we have counts for
8 papers
GIBLy: Improving 3D Semantic Segmentation through an Architecture-Agnostic Lightweight Geometric Inductive Bias Layer
Diogo Lavado, Alessandra Micheletti, Clàudia Soares
In 3D scene understanding, deep learning models rely on large models and extensive training to capture basic geometric structures that are present in the 3D data. However, existing…
The impact of abnormal temperatures on crop yields in Italy: a functional quantile regression approach
Giovanni Bocchi, Alessandra Micheletti, Paolo Nota +1
In this study, we apply functional regression analysis to identify the specific within-season periods during which temperature and precipitation anomalies most affect crop yields.…
GENEOnet: Statistical analysis supporting explainability and trustworthiness
Giovanni Bocchi, Patrizio Frosini, Alessandra Micheletti +5
Group Equivariant Non-Expansive Operators (GENEOs) have emerged as mathematical tools for constructing networks for Machine Learning and Artificial Intelligence. Recent findings su…
Enhancing Power Grid Inspections with Machine Learning
Diogo Lavado, Ricardo Santos, Andre Coelho +3
Ensuring the safety and reliability of power grids is critical as global energy demands continue to rise. Traditional inspection methods, such as manual observations or helicopter…
TS40K: a 3D Point Cloud Dataset of Rural Terrain and Electrical Transmission System
Diogo Lavado, Cláudia Soares, Alessandra Micheletti +3
Research on supervised learning algorithms in 3D scene understanding has risen in prominence and witness great increases in performance across several datasets. The leading force o…
Achieving Constraints in Neural Networks: A Stochastic Augmented Lagrangian Approach
Diogo Lavado, Cláudia Soares, Alessandra Micheletti
Regularizing Deep Neural Networks (DNNs) is essential for improving generalizability and preventing overfitting. Fixed penalty methods, though common, lack adaptability and suffer…
Low-Resource White-Box Semantic Segmentation of Supporting Towers on 3D Point Clouds via Signature Shape Identification
Diogo Lavado, Cláudia Soares, Alessandra Micheletti +4
Research in 3D semantic segmentation has been increasing performance metrics, like the IoU, by scaling model complexity and computational resources, leaving behind researchers and…
GENEOnet: A new machine learning paradigm based on Group Equivariant Non-Expansive Operators. An application to protein pocket detection
Giovanni Bocchi, Patrizio Frosini, Alessandra Micheletti +5
Nowadays there is a big spotlight cast on the development of techniques of explainable machine learning. Here we introduce a new computational paradigm based on Group Equivariant N…