13 citations · 18 across the 3 of their papers we have counts for
9 papers · 1 filter
Relational Concept Bottleneck Models
Pietro Barbiero, Francesco Giannini, Gabriele Ciravegna +2
The design of interpretable deep learning models working in relational domains poses an open challenge: interpretable deep learning methods, such as Concept Bottleneck Models (CBMs…
Local Propagation in Constraint-based Neural Network
Giuseppe Marra, Matteo Tiezzi, Stefano Melacci +3
In this paper we study a constraint-based representation of neural network architectures. We cast the learning problem in the Lagrangian framework and we investigate a simple optim…
A Lagrangian Approach to Information Propagation in Graph Neural Networks
Matteo Tiezzi, Giuseppe Marra, Stefano Melacci +2
In many real world applications, data are characterized by a complex structure, that can be naturally encoded as a graph. In the last years, the popularity of deep learning techniq…
On the relation between Loss Functions and T-Norms
Francesco Giannini, Giuseppe Marra, Michelangelo Diligenti +2
Deep learning has been shown to achieve impressive results in several domains like computer vision and natural language processing. A key element of this success has been the devel…
Neural Markov Logic Networks
Giuseppe Marra, Ondřej Kuželka
We introduce neural Markov logic networks (NMLNs), a statistical relational learning system that borrows ideas from Markov logic. Like Markov logic networks (MLNs), NMLNs are an ex…
LYRICS: a General Interface Layer to Integrate Logic Inference and Deep Learning
Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti +1
In spite of the amazing results obtained by deep learning in many applications, a real intelligent behavior of an agent acting in a complex environment is likely to require some ki…