72 citations · 159 across the 10 of their papers we have counts for
10 papers
Domyn-Small: A European 10B Reasoning Language Model
Simone Angarano, Francesco Bertolotti, Federico D'Ambrosio +7
We introduce Domyn-Small, a 10-billion-parameter open-weight reasoning language model released under the MIT license. Domyn-Small is the product of an initial pre-training phase on…
Message-Passing on Hypergraphs: Detectability, Phase Transitions and Higher-Order Information
Nicolò Ruggeri, Alessandro Lonardi, Caterina De Bacco
Hypergraphs are widely adopted tools to examine systems with higher-order interactions. Despite recent advancements in methods for community detection in these systems, we still la…
Structure and inference in hypergraphs with node attributes
Anna Badalyan, Nicolò Ruggeri, Caterina De Bacco
Many networked datasets with units interacting in groups of two or more, encoded with hypergraphs, are accompanied by extra information about nodes, such as the role of an individu…
Hypergraphx: a library for higher-order network analysis
Quintino Francesco Lotito, Martina Contisciani, Caterina De Bacco +6
From social to biological systems, many real-world systems are characterized by higher-order, non-dyadic interactions. Such systems are conveniently described by hypergraphs, where…
Community Detection in Large Hypergraphs
Nicolò Ruggeri, Martina Contisciani, Federico Battiston +1
Hypergraphs, describing networks where interactions take place among any number of units, are a natural tool to model many real-world social and biological systems. In this work we…
A framework to generate hypergraphs with community structure
Nicolò Ruggeri, Federico Battiston, Caterina De Bacco
In recent years hypergraphs have emerged as a powerful tool to study systems with multi-body interactions which cannot be trivially reduced to pairs. While highly structured method…
Provable concept learning for interpretable predictions using variational autoencoders
Armeen Taeb, Nicolo Ruggeri, Carina Schnuck +1
In safety-critical applications, practitioners are reluctant to trust neural networks when no interpretable explanations are available. Many attempts to provide such explanations r…
Fast Rates for Noisy Interpolation Require Rethinking the Effects of Inductive Bias
Konstantin Donhauser, Nicolo Ruggeri, Stefan Stojanovic +1
Good generalization performance on high-dimensional data crucially hinges on a simple structure of the ground truth and a corresponding strong inductive bias of the estimator. Even…
Sampling on networks: estimating spectral centrality measures and their impact in evaluating other relevant network measures
Nicolò Ruggeri, Caterina De Bacco
We perform an extensive analysis of how sampling impacts the estimate of several relevant network measures. In particular, we focus on how a sampling strategy optimized to recover…
Sampling on networks: estimating eigenvector centrality on incomplete graphs
Nicolò Ruggeri, Caterina De Bacco
We develop a new sampling method to estimate eigenvector centrality on incomplete networks. Our goal is to estimate this global centrality measure having at disposal a limited amou…