13 citations · 16 across the 6 of their papers we have counts for
6 papers
Weakly Supervised Segmentation as Semantic-Based Regularization
Stefano Colamonaco, Andrei-Bogdan Florea, Jaron Maene
Weakly supervised semantic segmentation (WSSS) trains dense pixel-level segmentation models from partial or coarse annotations such as bounding boxes, scribbles, or image-level tag…
Prototype-Grounded Concept Models for Verifiable Concept Alignment
Stefano Colamonaco, David Debot, Pietro Barbiero +1
Concept Bottleneck Models (CBMs) aim to improve interpretability in Deep Learning by structuring predictions through human-understandable concepts, but they provide no way to verif…
DeepLog: A Software Framework for Modular Neurosymbolic AI
Robin Manhaeve, Stefano Colamonaco, Vincent Derkinderen +4
DeepLog is an operational neurosymbolic framework that unifies logic and deep learning within standard PyTorch workflows. While existing neurosymbolic systems focus on a particular…
Neurosymbolic Object-Centric Learning with Distant Supervision
Stefano Colamonaco, David Debot, Giuseppe Marra
Neurosymbolic learning can use symbolic rules to provide supervision for latent concepts from weak labels, but it commonly assumes that the entities referenced by these rules are a…
Wind speed super-resolution and validation: from ERA5 to CERRA via diffusion models
Fabio Merizzi, Andrea Asperti, Stefano Colamonaco
The Copernicus Regional Reanalysis for Europe, CERRA, is a high-resolution regional reanalysis dataset for the European domain. In recent years it has shown significant utility acr…
Precipitation nowcasting with generative diffusion models
Andrea Asperti, Fabio Merizzi, Alberto Paparella +3
In recent years traditional numerical methods for accurate weather prediction have been increasingly challenged by deep learning methods. Numerous historical datasets used for shor…