no code implementations • 5 May 2024 • Peter Anthony, Francesco Giannini, Michelangelo Diligenti, Martin Homola, Marco Gori, Stefan Balogh, Jan Mojzis
Moreover, we introduce a tailored version of LENs that is shown to generate logic explanations with higher fidelity with respect to the model's predictions.
no code implementations • 16 Feb 2024 • Michelangelo Diligenti, Marco Gori, Marco Maggini, Leonardo Rigutini
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.
no code implementations • 6 Nov 2023 • Michelangelo Diligenti, Marco Gori, Marco Maggini, Leonardo Rigutini
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 kernel machines.
no code implementations • 23 Aug 2023 • Pietro Barbiero, Francesco Giannini, Gabriele Ciravegna, Michelangelo Diligenti, Giuseppe Marra
The design of interpretable deep learning models working in relational domains poses an open challenge: interpretable deep learning methods, such as Concept-Based Models (CBMs), are not designed to solve relational problems, while relational models are not as interpretable as CBMs.
no code implementations • 23 Mar 2023 • Michelangelo Diligenti, Francesco Giannini, Stefano Fioravanti, Caterina Graziani, Moreno Falaschi, Giuseppe Marra
In this paper, we exploit logic rules to enhance the embedding representations of KGEs on the PharmKG dataset.
1 code implementation • 19 Sep 2022 • Mateo Espinosa Zarlenga, Pietro Barbiero, Gabriele Ciravegna, Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, Zohreh Shams, Frederic Precioso, Stefano Melacci, Adrian Weller, Pietro Lio, Mateja Jamnik
Deploying AI-powered systems requires trustworthy models supporting effective human interactions, going beyond raw prediction accuracy.
no code implementations • 1 Jun 2021 • Giuseppe Marra, Michelangelo Diligenti, Francesco Giannini
However, they have been struggling at both dealing with the intrinsic uncertainty of the observations and scaling to real-world applications.
2 code implementations • 10 Nov 2020 • Maxime Mulamba, Jayanta Mandi, Michelangelo Diligenti, Michele Lombardi, Victor Bucarey, Tias Guns
Many decision-making processes involve solving a combinatorial optimization problem with uncertain input that can be estimated from historic data.
no code implementations • 6 Feb 2020 • Giuseppe Marra, Michelangelo Diligenti, Francesco Giannini, Marco Gori, Marco Maggini
Deep learning has been shown to achieve impressive results in several tasks where a large amount of training data is available.
no code implementations • 26 Jul 2019 • Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, Marco Maggini, Marco Gori
Neural-symbolic approaches have recently gained popularity to inject prior knowledge into a learner without requiring it to induce this knowledge from data.
no code implementations • 18 Jul 2019 • Francesco Giannini, Giuseppe Marra, Michelangelo Diligenti, Marco Maggini, Marco Gori
Deep learning has been shown to achieve impressive results in several domains like computer vision and natural language processing.
no code implementations • 18 Mar 2019 • Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, Marco Gori
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 kind of higher-level symbolic inference.
no code implementations • 14 Jan 2019 • Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, Marco Gori
Deep learning is very effective at jointly learning feature representations and classification models, especially when dealing with high dimensional input patterns.
no code implementations • 16 Jul 2018 • Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, Marco Gori
We use deep architectures to model the involved variables, and propose a computational scheme where the learning process carries out a satisfaction of the constraints.