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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.

1 code implementation • 7 Feb 2022 • Eleonora Misino, Giuseppe Marra, Emanuele Sansone

To the best of our knowledge, this work is the first to propose a general-purpose end-to-end framework integrating probabilistic logic programming into a deep generative model.

no code implementations • 25 Aug 2021 • Giuseppe Marra, Sebastijan Dumančić, Robin Manhaeve, Luc De Raedt

Neural-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning.

1 code implementation • 23 Jun 2021 • Thomas Winters, Giuseppe Marra, Robin Manhaeve, Luc De Raedt

Like graphical models, these probabilistic logic programs define a probability distribution over possible worlds, for which inference is computationally hard.

no code implementations • 1 Jun 2021 • Giuseppe Marra, Michelangelo Diligenti, Francesco Giannini

Unlike flat architectures like Knowledge Graph Embedders, which can only represent relations between entities, R2Ns define an additional computational structure, accounting for higher-level relations among the ground atoms.

no code implementations • 26 Sep 2020 • Gavin Rens, Jean-François Raskin, Raphaël Reynouad, Giuseppe Marra

In our formal setting, we consider a Markov decision process (MDP) that models the dynamics of the environment in which the agent evolves and a Mealy machine synchronized with this MDP to formalize the non-Markovian reward function.

1 code implementation • 5 May 2020 • Matteo Tiezzi, Giuseppe Marra, Stefano Melacci, Marco Maggini

The popularity of deep learning techniques renewed the interest in neural architectures able to process complex structures that can be represented using graphs, inspired by Graph Neural Networks (GNNs).

no code implementations • 18 Mar 2020 • Luc De Raedt, Sebastijan Dumančić, Robin Manhaeve, Giuseppe Marra

Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning.

no code implementations • 18 Feb 2020 • Giuseppe Marra, Matteo Tiezzi, Stefano Melacci, Alessandro Betti, Marco Maggini, Marco Gori

In this paper we study a constraint-based representation of neural network architectures.

1 code implementation • 18 Feb 2020 • Matteo Tiezzi, Giuseppe Marra, Stefano Melacci, Marco Maggini, Marco Gori

GNNs exploit a set of state variables, each assigned to a graph node, and a diffusion mechanism of the states among neighbor nodes, to implement an iterative procedure to compute the fixed point of the (learnable) state transition function.

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 • 6 Sep 2019 • Marco Maggini, Giuseppe Marra, Stefano Melacci, Andrea Zugarini

We consider a scenario where an artificial agent is reading a stream of text composed of a set of narrations, and it is informed about the identity of some of the individuals that are mentioned in the text portion that is currently being read.

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 • 19 Jul 2019 • Giuseppe Marra, Andrea Zugarini, Stefano Melacci, Marco Maggini

In the last few years, neural networks have been intensively used to develop meaningful distributed representations of words and contexts around them.

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 • ICLR 2020 • Giuseppe Marra, Ondřej Kuželka

We introduce neural Markov logic networks (NMLNs), a statistical relational learning system that borrows ideas from Markov logic.

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 • 21 Aug 2018 • Alessandro Betti, Marco Gori, Giuseppe Marra

This might open the doors to a truly novel class of learning algorithms where, because of the introduction of the notion of support neurons, the optimization scheme also plays a fundamental role in the construction of the architecture.

no code implementations • ICLR 2019 • Giuseppe Marra, Dario Zanca, Alessandro Betti, Marco Gori

The effectiveness of deep neural architectures has been widely supported in terms of both experimental and foundational principles.

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.

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