227 citations · 270 across the 8 of their papers we have counts for
6 papers · 1 filter
Multitask Kernel-based Learning with Logic Constraints
Michelangelo Diligenti, Marco Gori, Marco Maggini +1
This paper presents a general framework to integrate prior knowledge in the form of logic constraints among a set of task functions into kernel machines. The logic propositions pro…
Multitask Kernel-based Learning with First-Order Logic Constraints
Michelangelo Diligenti, Marco Gori, Marco Maggini +1
In this paper we propose a general framework to integrate supervised and unsupervised examples with background knowledge expressed by a collection of first-order logic clauses into…
Focus of Attention Improves Information Transfer in Visual Features
Matteo Tiezzi, Stefano Melacci, Alessandro Betti +2
Unsupervised learning from continuous visual streams is a challenging problem that cannot be naturally and efficiently managed in the classic batch-mode setting of computation. The…
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…