Marco Gori — accepted AI-conference papers

2026

DeepProofLog: Efficient Proving in Deep Stochastic Logic Programs

Ying Jiao, Rodrigo Castellano Ontiveros, Luc De Raedt, Marco Gori, Francesco Giannini, Michelangelo Diligenti +1

AAAI 2026technical

Neurosymbolic (NeSy) AI combines neural architectures and symbolic reasoning to improve accuracy, interpretability, and generalization. While logic inference on top of subsymbolic modules has been shown to effectively guarantee these properties, this often comes at the cost of reduced scalability, w

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2025

Grounding Methods for Neural-Symbolic AI

Rodrigo Castellano Ontiveros, Francesco Giannini, Marco Gori, Giuseppe Marra, Michelangelo Diligenti

IJCAI 2025

A large class of Neural-Symbolic (NeSy) methods employs a machine learner to process the input entities, while relying on a reasoner based on First-Order Logic to represent and process more complex relationships among the entities. A fundamental role for these methods is played by the process of log

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2024

Clue-Instruct: Text-Based Clue Generation for Educational Crossword Puzzles

Andrea Zugarini, Kamyar Zeinalipour, Surya Sai Kadali, Marco Maggini, Marco Gori, Leonardo Rigutini

COLING 2024main

Crossword puzzles are popular linguistic games often used as tools to engage students in learning. Educational crosswords are characterized by less cryptic and more factual clues that distinguish them from traditional crossword puzzles. Despite there exist several publicly available clue-answer pair…

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2024

Nature-Inspired Local Propagation

Alessandro Betti, Marco Gori

NeurIPS 2024poster

The spectacular results achieved in machine learning, including the recent advances in generative AI, rely on large data collections. On the opposite, intelligent processes in nature arises without the need for such collections, but simply by on-line processing of the environmental information. In p…

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2024

Neural Time-Reversed Generalized Riccati Equation

Alessandro Betti, Michele Casoni, Marco Gori, Simone Marullo, Stefano Melacci, Matteo Tiezzi

AAAI 2024technical

Optimal control deals with optimization problems in which variables steer a dynamical system, and its outcome contributes to the objective function. Two classical approaches to solving these problems are Dynamic Programming and the Pontryagin Maximum Principle. In both approaches, Hamiltonian equati…

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2022

Being Friends Instead of Adversaries: Deep Networks Learn from Data Simplified by Other Networks

Simone Marullo, Matteo Tiezzi, Marco Gori, Stefano Melacci

AAAI 2022technical

Amongst a variety of approaches aimed at making the learning procedure of neural networks more effective, the scientific community developed strategies to order the examples according to their estimated complexity, to distil knowledge from larger networks, or to exploit the principles behind adversa…

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2022

Entropy-Based Logic Explanations of Neural Networks

Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Pietro Lió, Marco Gori, Stefano Melacci

AAAI 2022technical

Explainable artificial intelligence has rapidly emerged since lawmakers have started requiring interpretable models for safety-critical domains. Concept-based neural networks have arisen as explainable-by-design methods as they leverage human-understandable symbols (i.e. concepts) to predict class m…

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2020

Focus of Attention Improves Information Transfer in Visual Features

Matteo Tiezzi, Stefano Melacci, Alessandro Betti, Marco Maggini, Marco Gori

NeurIPS 2020poster

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 information stream must be carefully processed accordingly to an appropriate spatio-temporal distribution of the visua…

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2020

Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective

Luís C. Lamb, Artur d’Avila Garcez, Marco Gori, Marcelo O.R. Prates, Pedro H.C. Avelar, Moshe Y. Vardi

IJCAI 2020poster

Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNNs) have been widely used in relational and symbolic domains, with widespread application of GNNs in combinatorial optimization, constraint satisfaction, r…

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2020

Human-Driven FOL Explanations of Deep Learning

Gabriele Ciravegna, Francesco Giannini, Marco Gori, Marco Maggini, Stefano Melacci

IJCAI 2020poster

Deep neural networks are usually considered black-boxes due to their complex internal architecture, that cannot straightforwardly provide human-understandable explanations on how they behave. Indeed, Deep Learning is still viewed with skepticism in those real-world domains in which incorrect predict…

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2017

Variational Laws of Visual Attention for Dynamic Scenes

Dario Zanca, Marco Gori

NeurIPS 2017poster

Computational models of visual attention are at the crossroad of disciplines like cognitive science, computational neuroscience, and computer vision. This paper proposes a model of attentional scanpath that is based on the principle that there are foundational laws that drive the emergence of visual…

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