Federico Siciliano

About Federico Siciliano

Federico Siciliano, With an exceptional h-index of 4 and a recent h-index of 4 (since 2020), a distinguished researcher at Sapienza Università di Roma, specializes in the field of Data Science, Machine Learning, Neural Networks.

His recent articles reflect a diverse array of research interests and contributions to the field:

Investigating the Robustness of Sequential Recommender Systems Against Training Data Perturbations

Architectural components of trustworthy Artificial Intelligence

The Power of Noise: Redefining Retrieval for RAG Systems

A graph neural network-based model with Out-of-Distribution Robustness for enhancing Antiretroviral Therapy Outcome Prediction for HIV-1

Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It

A data-driven approach to refine predictions of differentiated thyroid cancer outcomes: a prospective multicenter study

Concept Distillation in Graph Neural Networks

The CAESAR Project for the ASI Space Weather Infrastructure

Federico Siciliano Information

University

Position

PhD student in Data Science

Citations(all)

75

Citations(since 2020)

75

Cited By

0

hIndex(all)

4

hIndex(since 2020)

4

i10Index(all)

2

i10Index(since 2020)

2

Email

University Profile Page

Google Scholar

Federico Siciliano Skills & Research Interests

Data Science

Machine Learning

Neural Networks

Top articles of Federico Siciliano

Investigating the Robustness of Sequential Recommender Systems Against Training Data Perturbations

arXiv preprint arXiv:2307.13165

2023/7/24

Architectural components of trustworthy Artificial Intelligence

2024/1/31

Federico Siciliano
Federico Siciliano

H-Index: 0

The Power of Noise: Redefining Retrieval for RAG Systems

arXiv preprint arXiv:2401.14887

2024/1/26

A graph neural network-based model with Out-of-Distribution Robustness for enhancing Antiretroviral Therapy Outcome Prediction for HIV-1

arXiv preprint arXiv:2312.17506

2023/12/29

Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It

arXiv preprint arXiv:2312.15228

2023/12/23

A data-driven approach to refine predictions of differentiated thyroid cancer outcomes: a prospective multicenter study

The Journal of Clinical Endocrinology & Metabolism

2023/8

Concept Distillation in Graph Neural Networks

2023/7/26

Federico Siciliano
Federico Siciliano

H-Index: 0

Fabrizio Silvestri
Fabrizio Silvestri

H-Index: 24

RRAML: Reinforced Retrieval Augmented Machine Learning

arXiv preprint arXiv:2307.12798

2023/7/24

Integrating Item Relevance in Training Loss for Sequential Recommender Systems

2023/9/14

Deep active learning for misinformation detection using geometric deep learning

Online Social Networks and Media

2023/1/1

Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels

2023

FbMultiLingMisinfo: Challenging Large-Scale Multilingual Benchmark for Misinformation Detection

2022/7/18

Newron: a new generalization of the artificial neuron to enhance the interpretability of neural networks

2022/7/18

Adata-driven approach reveals emerging risk factors for recurrent and persistent differentiated thyroid cancer

Endocrine Abstracts

2022/5/7

Comparing long short-term memory and convolutional neural networks in SYM-H index forecasting

EGU General Assembly Conference Abstracts

2022/5

Federico Siciliano
Federico Siciliano

H-Index: 0

Leveraging Deep Learning models to assess the temporal validity of Emotional Text Mining procedures

2022

Francesca Greco
Francesca Greco

H-Index: 7

Federico Siciliano
Federico Siciliano

H-Index: 0

Forecasting SYM‐H Index: A Comparison Between Long Short‐Term Memory and Convolutional Neural Networks

Space Weather

2021/2

See List of Professors in Federico Siciliano University(Sapienza Università di Roma)

Co-Authors

academic-engine