no code implementations • 9 Apr 2024 • Andrea Zugarini, Kamyar Zeinalipour, Surya Sai Kadali, Marco Maggini, Marco Gori, Leonardo Rigutini
By gathering from Wikipedia pages informative content associated with relevant keywords, we use Large Language Models to automatically generate pedagogical clues related to the given input keyword and its context.
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 • 16 Feb 2024 • Achille Globo, Antonio Trevisi, Andrea Zugarini, Leonardo Rigutini, Marco Maggini, Stefano Melacci
In this paper we present a method for the automatic generation of large aligned corpora, that is based on the assumption that news and blog websites talk about the same events using different narrative styles.
no code implementations • 3 Dec 2023 • Kamyar Zeinalipour, Mohamed Zaky Saad, Marco Maggini, Marco Gori
This paper presents the first Arabic crossword puzzle generator driven by advanced AI technology.
no code implementations • 27 Nov 2023 • Kamyar Zeinalipour, Tommaso laquinta, Asya Zanollo, Giovanni Angelini, Leonardo Rigutini, Marco Maggini, Marco Gori
On the other hand, for generating crossword clues from a given text, Zero/Few-shot learning techniques were used to extract clues from the input text, adding variety and creativity to the puzzles.
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 • 3 Nov 2023 • Leonardo Rigutini, Tiziano Papini, Marco Maggini, Franco Scarselli
Two main approaches exist in literature for the task of learning to rank: 1) a score function, learned by examples, which evaluates the properties of each object yielding an absolute relevance value that can be used to order the objects or 2) a pairwise approach, where a "preference function" is learned using pairs of objects to define which one has to be ranked first.
1 code implementation • 11 Aug 2021 • Gabriele Ciravegna, Pietro Barbiero, Francesco Giannini, Marco Gori, Pietro Lió, Marco Maggini, Stefano Melacci
The language used to communicate the explanations must be formal enough to be implementable in a machine and friendly enough to be understandable by a wide audience.
no code implementations • 8 Feb 2021 • Andrea Zugarini, Luca Pasqualini, Stefano Melacci, Marco Maggini
Writers, poets, singers usually do not create their compositions in just one breath.
2 code implementations • VarDial (COLING) 2020 • Andrea Zugarini, Matteo Tiezzi, Marco Maggini
Italian is a Romance language that has its roots in Vulgar Latin.
no code implementations • NeurIPS 2020 • Matteo Tiezzi, Stefano Melacci, Alessandro Betti, Marco Maggini, Marco Gori
In order to better structure the input probability distribution, we use a human-like focus of attention model that, coherently with the information maximization model, is also based on second-order differential equations.
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 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 • 6 Sep 2019 • Matteo Tiezzi, Stefano Melacci, Marco Maggini, Angelo Frosini
In this paper we describe a video surveillance system able to detect traffic events in videos acquired by fixed videocameras on highways.
no code implementations • 31 Aug 2019 • Francesco Giannini, Marco Maggini
A main property of support vector machines consists in the fact that only a small portion of the training data is significant to determine the maximum margin separating hyperplane in the feature space, the so called support vectors.
no code implementations • 23 Aug 2019 • Andrea Zugarini, Stefano Melacci, Marco Maggini
Motivated by the recent progresses on machine learning-based models that learn artistic styles, in this paper we focus on the problem of poem generation.
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 • 9 Jan 2017 • Marco Gori, Marco Maggini, Alessandro Rossi
In this document we shows a first implementation and some preliminary results of a new theory, facing Machine Learning problems in the frameworks of Classical Mechanics and Variational Calculus.
no code implementations • 3 Jan 2017 • Marco Gori, Marco Maggini, Alessandro Rossi
We analyze a new approach to Machine Learning coming from a modification of classical regularization networks by casting the process in the time dimension, leading to a sort of collapse of dimensionality in the problem of learning the model parameters.
no code implementations • 11 Aug 2014 • Marco Gori, Marco Lippi, Marco Maggini, Stefano Melacci
In the last few years we have seen a growing interest in machine learning approaches to computer vision and, especially, to semantic labeling.