Gabriele Ciravegna

Orcid: 0000-0002-6799-1043

Affiliations:
  • Politecnico di Torino, Turin, Italy


According to our database1, Gabriele Ciravegna authored at least 43 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2024
Graph Neural Networks for Graph Drawing.
IEEE Trans. Neural Networks Learn. Syst., April, 2024

Gradient-Based Competitive Learning: Theory.
Cogn. Comput., March, 2024

A new perspective on optimizers: leveraging moreau-yosida approximation in gradient-based learning.
Intelligenza Artificiale, 2024

Interpretable Concept-Based Memory Reasoning.
CoRR, 2024

Voice Disorder Analysis: a Transformer-based Approach.
CoRR, 2024

Self-supervised Interpretable Concept-based Models for Text Classification.
CoRR, 2024

Workshop on Human-Interpretable AI.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Non-invasive AI-powered Diagnostics: The case of Voice-Disorder Detection.
Proceedings of the Workshops of the EDBT/ICDT 2024 Joint Conference co-located with the EDBT/ICDT 2024 Joint Conference, 2024

2023
Learning Logic Explanations by Neural Networks.
Proceedings of the Compendium of Neurosymbolic Artificial Intelligence, 2023

Concept-based Explainable Artificial Intelligence: A Survey.
CoRR, 2023

Relational Concept Based Models.
CoRR, 2023

Logic Explained Networks.
Artif. Intell., 2023

Concept Distillation in Graph Neural Networks.
Proceedings of the Explainable Artificial Intelligence, 2023

Knowledge-Driven Active Learning.
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Interpretable Neural-Symbolic Concept Reasoning.
Proceedings of the International Conference on Machine Learning, 2023

Toward Novel Optimizers: A Moreau-Yosida View of Gradient-Based Learning.
Proceedings of the AIxIA 2023 - Advances in Artificial Intelligence, 2023

2022
On the Two-fold Role of Logic Constraints in Deep Learning.
PhD thesis, 2022

Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

A survey on data integration for multi-omics sample clustering.
Neurocomputing, 2022

Concept Embedding Models.
CoRR, 2022

Encoding Concepts in Graph Neural Networks.
CoRR, 2022

Encryption-agnostic classifiers of traffic originators and their application to anomaly detection.
Comput. Electr. Eng., 2022

Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Extending Logic Explained Networks to Text Classification.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

Minimizing Cross Intersections in Graph Drawing via Linear Splines.
Proceedings of the Artificial Neural Networks in Pattern Recognition, 2022

Entropy-Based Logic Explanations of Neural Networks.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Knowledge-driven Active Learning.
CoRR, 2021

LENs: a Python library for Logic Explained Networks.
CoRR, 2021

Topological Gradient-based Competitive Learning.
Proceedings of the International Joint Conference on Neural Networks, 2021

2020
Assessing Discriminating Capability of Geometrical Descriptors for 3D Face Recognition by Using the GH-EXIN Neural Network.
Proceedings of the Neural Approaches to Dynamics of Signal Exchanges, 2020

Understanding Cancer Phenomenon at Gene Expression Level by using a Shallow Neural Network Chain.
Proceedings of the Neural Approaches to Dynamics of Signal Exchanges, 2020

DNA Microarray Classification: Evolutionary Optimization of Neural Network Hyper-parameters.
Proceedings of the Neural Approaches to Dynamics of Signal Exchanges, 2020

The GH-EXIN neural network for hierarchical clustering.
Neural Networks, 2020

Gradient-based Competitive Learning: Theory.
CoRR, 2020

Topological Gradient-based Competitive Learning.
CoRR, 2020

Can Domain Knowledge Alleviate Adversarial Attacks in Multi-Label Classifiers?
CoRR, 2020

Human-Driven FOL Explanations of Deep Learning.
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Unsupervised Multi-omic Data Fusion: The Neural Graph Learning Network.
Proceedings of the Intelligent Computing Theories and Application, 2020

A Constraint-Based Approach to Learning and Explanation.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Supervised Gene Identification in Colorectal Cancer.
Proceedings of the Quantifying and Processing Biomedical and Behavioral Signals, 2019

Unsupervised Gene Identification in Colorectal Cancer.
Proceedings of the Quantifying and Processing Biomedical and Behavioral Signals, 2019

2018
Nonstationary topological learning with bridges and convex polytopes: the G-EXIN neural network.
Proceedings of the 2018 International Joint Conference on Neural Networks, 2018

2017
Discovering Hierarchical Neural Archetype Sets.
Proceedings of the Advances in Intelligent Information Hiding and Multimedia Signal Processing, 2017


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