Search Results for author: Gabriele Ciravegna

Found 15 papers, 9 papers with code

Concept-based Explainable Artificial Intelligence: A Survey

no code implementations20 Dec 2023 Eleonora Poeta, Gabriele Ciravegna, Eliana Pastor, Tania Cerquitelli, Elena Baralis

The field of explainable artificial intelligence emerged in response to the growing need for more transparent and reliable models.

Explainable artificial intelligence

Relational Concept Based Models

no code implementations23 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.

Image Classification Knowledge Graphs +1

Extending Logic Explained Networks to Text Classification

1 code implementation4 Nov 2022 Rishabh Jain, Gabriele Ciravegna, Pietro Barbiero, Francesco Giannini, Davide Buffelli, Pietro Lio

Recently, Logic Explained Networks (LENs) have been proposed as explainable-by-design neural models providing logic explanations for their predictions.

text-classification Text Classification

Knowledge-driven Active Learning

1 code implementation15 Oct 2021 Gabriele Ciravegna, Frédéric Precioso, Alessandro Betti, Kevin Mottin, Marco Gori

The deployment of Deep Learning (DL) models is still precluded in those contexts where the amount of supervised data is limited.

Active Learning Multi-Label Classification +2

Graph Neural Networks for Graph Drawing

no code implementations21 Sep 2021 Matteo Tiezzi, Gabriele Ciravegna, Marco Gori

Graph Drawing techniques have been developed in the last few years with the purpose of producing aesthetically pleasing node-link layouts.

Logic Explained Networks

1 code implementation11 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.

Explainable artificial intelligence

Entropy-based Logic Explanations of Neural Networks

3 code implementations12 Jun 2021 Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Pietro Lió, Marco Gori, Stefano Melacci

Explainable artificial intelligence has rapidly emerged since lawmakers have started requiring interpretable models for safety-critical domains.

Explainable artificial intelligence Image Classification

Gradient-based Competitive Learning: Theory

no code implementations6 Sep 2020 Giansalvo Cirrincione, Pietro Barbiero, Gabriele Ciravegna, Vincenzo Randazzo

The former is just an adaptation of a standard competitive layer for deep clustering, while the latter is trained on the transposed matrix.

Clustering Deep Clustering +1

Topological Gradient-based Competitive Learning

1 code implementation21 Aug 2020 Pietro Barbiero, Gabriele Ciravegna, Vincenzo Randazzo, Giansalvo Cirrincione

The aim of this work is to present a novel comprehensive theory aspiring at bridging competitive learning with gradient-based learning, thus allowing the use of extremely powerful deep neural networks for feature extraction and projection combined with the remarkable flexibility and expressiveness of competitive learning.

Clustering Deep Clustering

Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers

no code implementations6 Jun 2020 Stefano Melacci, Gabriele Ciravegna, Angelo Sotgiu, Ambra Demontis, Battista Biggio, Marco Gori, Fabio Roli

Adversarial attacks on machine learning-based classifiers, along with defense mechanisms, have been widely studied in the context of single-label classification problems.

Multi-Label Classification

The GH-EXIN neural network for hierarchical clustering

1 code implementation Neural Networks 2020 Giansalvo Cirrincione, Gabriele Ciravegna, Pietro Barbiero, Vincenzo Randazzo, Eros Pasero

Furthermore, an important and very promising application of GH-EXIN in two-way hierarchical clustering, for the analysis of gene expression data in the study of the colorectal cancer is described.

Clustering Self-Organized Clustering

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