Federico Siciliano

Hi! I’m Federico Siciliano, a PhD Student in Data Science at Sapienza University of Rome. My research interests include Explainable Artificial Intelligence, Foundations of Deep Learning and Information Retrieval. I’m part of the RSTLess research group, which focuses on Robust, Safe and Transparent Deep Learning. Prior to this, I completed my Master’s Degree in Data Science at Sapienza University of Rome.

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The Power of Noise: Redefining Retrieval for RAG Systems
Cuconasu, Florin; Trappolini, Giovanni; Siciliano, Federico; Filice, Simone; Campagnano, Cesare; Maarek, Yoelle; Tonellotto, Nicola; Silvestri, Fabrizio
Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels
Bucarelli, Maria Sofia; Cassano, Lucas; Siciliano, Federico; Mantrach, Amin; Silvestri, Fabrizio;
Deep active learning for misinformation detection using geometric deep learning
Barnabò, Giorgio; Siciliano, Federico; Castillo, Carlos; Leonardi, Stefano; Nakov, Preslav; Da San Martino, Giovanni; Silvestri, Fabrizio;
A data-driven approach to refine predictions of differentiated thyroid cancer outcomes: a prospective multicenter study
Grani, Giorgio; Gentili, Michele; Siciliano, Federico; Albano, Domenico; Zilioli, Valentina; Morelli, Silvia; Puxeddu, Efisio; Zatelli, Maria Chiara; Gagliardi, Irene; Piovesan, Alessandro;
Integrating Item Relevance in Training Loss for Sequential Recommender Systems
Bacciu, Andrea; Siciliano, Federico; Tonellotto, Nicola; Silvestri, Fabrizio;
RRAML: Reinforced Retrieval Augmented Machine Learning
Bacciu, Andrea; Cuconasu, Florin; Siciliano, Federico; Silvestri, Fabrizio; Tonellotto, Nicola; Trappolini, Giovanni;
Investigating the Robustness of Sequential Recommender Systems Against Training Data Perturbations: an Empirical Study
Betello, Filippo; Siciliano, Federico; Mishra, Pushkar; Silvestri, Fabrizio;
Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It
Federico Siciliano, Luca Maiano, Lorenzo Papa, Irene Amerini, Fabrizio Silvestri
Adata-driven approach reveals emerging risk factors for recurrent and persistent differentiated thyroid cancer
Gentili, Michele; Grani, Giorgio; Siciliano, Federico;
Comparing long short-term memory and convolutional neural networks in SYM-H index forecasting
Siciliano, Federico; Consolini, Giuseppe; Giannattasio, Fabio;
Newron: a new generalization of the artificial neuron to enhance the interpretability of neural networks
Siciliano, Federico; Bucarelli, Maria Sofia; Tolomei, Gabriele; Silvestri, Fabrizio;
FbMultiLingMisinfo: Challenging Large-Scale Multilingual Benchmark for Misinformation Detection
Barnabò, Giorgio; Siciliano, Federico; Castillo, Carlos; Leonardi, Stefano; Nakov, Preslav; Da San Martino, Giovanni; Silvestri, Fabrizio;
Encoding Concepts in Graph Neural Networks
Magister, Lucie Charlotte; Barbiero, Pietro; Kazhdan, Dmitry; Siciliano, Federico; Ciravegna, Gabriele; Silvestri, Fabrizio; Jamnik, Mateja; Lio, Pietro;
Leveraging Deep Learning models to assess the temporal validity of Emotional Text Mining procedures
Greco, F; Polli, A; Siciliano, F;
Forecasting SYM‐H Index: A Comparison Between Long Short‐Term Memory and Convolutional Neural Networks
Siciliano, F; Consolini, Giuseppe; Tozzi, Roberta; Gentili, M; Giannattasio, Fabio; De Michelis, Paola;
Reconstruction of F-Region Electric Current Densities from more than 2 Years of Swarm Satellite Magnetic data
Tozzi, Roberta; Pezzopane, Michael; De Michelis, Paola; Pignalberi, Alessio; Siciliano, Federico;

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