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 • 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 • 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.
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 • 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 • 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 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 • 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 • 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 • 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 • 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.
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 • 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.
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.
no code implementations • 25 Aug 2021 • Giuseppe Marra, Sebastijan Dumančić, Robin Manhaeve, Luc De Raedt
This survey explores the integration of learning and reasoning in two different fields of artificial intelligence: neurosymbolic and statistical relational artificial intelligence.
no code implementations • 8 Mar 2023 • Lennert De Smet, Pedro Zuidberg Dos Martires, Robin Manhaeve, Giuseppe Marra, Angelika Kimmig, Luc De Raedt
Probabilistic NeSy focuses on integrating neural networks with both logic and probability theory, which additionally allows learning under uncertainty.
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.
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.
1 code implementation • 6 Mar 2023 • Wen-Chi Yang, Giuseppe Marra, Gavin Rens, Luc De Raedt
To this end, we introduce Probabilistic Logic Policy Gradient (PLPG).
1 code implementation • 2 Feb 2024 • Gabriele Dominici, Pietro Barbiero, Francesco Giannini, Martin Gjoreski, Giuseppe Marra, Marc Langheinrich
"), and imagine alternative scenarios that could result in different predictions (the "What if?").
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.
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).
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.
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.
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 • 27 Apr 2023 • Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Mateo Espinosa Zarlenga, Lucie Charlotte Magister, Alberto Tonda, Pietro Lio', Frederic Precioso, Mateja Jamnik, Giuseppe Marra
Deep learning methods are highly accurate, yet their opaque decision process prevents them from earning full human trust.