10 citations · 16 across the 11 of their papers we have counts for
4 papers · 1 filter
GEqTrain: A Configuration-Driven Framework for Retargeting Equivariant Graph Neural Networks Across 3D Scientific Tasks
Daniele Angioletti, Marco Nobile, Vittorio Limongelli
Equivariant graph neural networks provide a powerful modeling language for three-dimensional scientific data, but their reuse is often limited by implementations tied to specific t…
Investigating the performance of multi-objective optimization when learning Bayesian Networks
Paolo Cazzaniga, Marco S. Nobile, Daniele Ramazzotti
Bayesian Networks have been widely used in the last decades in many fields, to describe statistical dependencies among random variables. In general, learning the structure of such…
Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models
Daniele Ramazzotti, Marco S. Nobile, Paolo Cazzaniga +2
The emergence and development of cancer is a consequence of the accumulation over time of genomic mutations involving a specific set of genes, which provides the cancer clones with…
Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena
Daniele Ramazzotti, Marco S. Nobile, Marco Antoniotti +1
Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is further complicated by many theoretical issues, such as the I-equivalence among different structures.…