204 citations · 269 across the 17 of their papers we have counts for
18 papers
Efficient Recommendations via Graph Coarsening and Label Propagation
Alessandro Sbandi, Federico Siciliano, Fabrizio Silvestri
Graph-based recommendations are widely adopted in real-world industrial applications. However, graphs in these systems often reach a massive scale, posing notable scalability and e…
Concept-Enhanced Multimodal RAG: Towards Interpretable and Accurate Radiology Report Generation
Marco Salmè, Federico Siciliano, Fabrizio Silvestri +3
Radiology Report Generation (RRG) through Vision-Language Models (VLMs) promises to reduce documentation burden, improve reporting consistency, and accelerate clinical workflows. H…
AutoBench: Automating LLM Evaluation through Reciprocal Peer Assessment
Dario Loi, Elena Maria Muià, Federico Siciliano +4
We present AutoBench, a fully automated and self-sustaining framework for evaluating Large Language Models (LLMs) through reciprocal peer assessment. This paper provides a rigorous…
Titans Revisited: A Lightweight Reimplementation and Critical Analysis of a Test-Time Memory Model
Gavriel Di Nepi, Federico Siciliano, Fabrizio Silvestri
By the end of 2024, Google researchers introduced Titans: Learning at Test Time, a neural memory model achieving strong empirical results across multiple tasks. However, the lack o…
Directional Sheaf Hypergraph Networks: Unifying Learning on Directed and Undirected Hypergraphs
Emanuele Mule, Stefano Fiorini, Antonio Purificato +3
Hypergraphs provide a natural way to represent higher-order interactions among multiple entities. While undirected hypergraphs have been extensively studied, the case of directed h…
A Systematic Replicability and Comparative Study of BSARec and SASRec for Sequential Recommendation
Chiara D'Ercoli, Giulia Di Teodoro, Federico Siciliano
This study aims at comparing two sequential recommender systems: Self-Attention based Sequential Recommendation (SASRec), and Beyond Self-Attention based Sequential Recommendation…
Static Pruning in Dense Retrieval using Matrix Decomposition
Federico Siciliano, Francesca Pezzuti, Nicola Tonellotto +1
In the era of dense retrieval, document indexing and retrieval is largely based on encoding models that transform text documents into embeddings. The efficiency of retrieval is dir…
A Theoretical Analysis of Recommendation Loss Functions under Negative Sampling
Giulia Di Teodoro, Federico Siciliano, Nicola Tonellotto +1
Loss functions like Categorical Cross Entropy (CCE), Binary Cross Entropy (BCE), and Bayesian Personalized Ranking (BPR) are commonly used in training Recommender Systems (RSs) to…
A Reproducible Analysis of Sequential Recommender Systems
Filippo Betello, Antonio Purificato, Federico Siciliano +4
Sequential Recommender Systems (SRSs) have emerged as a highly efficient approach to recommendation systems. By leveraging sequential data, SRSs can identify temporal patterns in u…
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 +3
Fake news detection models are critical to countering disinformation but can be manipulated through adversarial attacks. In this position paper, we analyze how an attacker can comp…
The Power of Noise: Redefining Retrieval for RAG Systems
Florin Cuconasu, Giovanni Trappolini, Federico Siciliano +5
Retrieval-Augmented Generation (RAG) has recently emerged as a method to extend beyond the pre-trained knowledge of Large Language Models by augmenting the original prompt with rel…
A graph neural network-based model with Out-of-Distribution Robustness for enhancing Antiretroviral Therapy Outcome Prediction for HIV-1
Giulia Di Teodoro, Federico Siciliano, Valerio Guarrasi +6
Predicting the outcome of antiretroviral therapies (ART) for HIV-1 is a pressing clinical challenge, especially when the ART includes drugs with limited effectiveness data. This sc…
RRAML: Reinforced Retrieval Augmented Machine Learning
Andrea Bacciu, Florin Cuconasu, Federico Siciliano +3
The emergence of large language models (LLMs) has revolutionized machine learning and related fields, showcasing remarkable abilities in comprehending, generating, and manipulating…
Investigating the Robustness of Sequential Recommender Systems Against Training Data Perturbations
Filippo Betello, Federico Siciliano, Pushkar Mishra +1
Sequential Recommender Systems (SRSs) are widely employed to model user behavior over time. However, their robustness in the face of perturbations in training data remains a largel…
Integrating Item Relevance in Training Loss for Sequential Recommender Systems
Andrea Bacciu, Federico Siciliano, Nicola Tonellotto +1
Sequential Recommender Systems (SRSs) are a popular type of recommender system that learns from a user's history to predict the next item they are likely to interact with. However,…
Sheaf4Rec: Sheaf Neural Networks for Graph-based Recommender Systems
Antonio Purificato, Giulia Cassarà, Federico Siciliano +2
Recent advancements in Graph Neural Networks (GNN) have facilitated their widespread adoption in various applications, including recommendation systems. GNNs have proven to be effe…
Encoding Concepts in Graph Neural Networks
Lucie Charlotte Magister, Pietro Barbiero, Dmitry Kazhdan +5
The opaque reasoning of Graph Neural Networks induces a lack of human trust. Existing graph network explainers attempt to address this issue by providing post-hoc explanations, how…
NEWRON: A New Generalization of the Artificial Neuron to Enhance the Interpretability of Neural Networks
Federico Siciliano, Maria Sofia Bucarelli, Gabriele Tolomei +1
In this work, we formulate NEWRON: a generalization of the McCulloch-Pitts neuron structure. This new framework aims to explore additional desirable properties of artificial neuron…