12 citations · 29 across the 10 of their papers we have counts for
10 papers
ECLIPSE: Contrastive Dimension Importance Estimation with Pseudo-Irrelevance Feedback for Dense Retrieval
Giulio D'Erasmo, Giovanni Trappolini, Nicola Tonellotto +1
Recent advances in Information Retrieval have leveraged high-dimensional embedding spaces to improve the retrieval of relevant documents. Moreover, the Manifold Clustering Hypothes…
Prompt-to-OS (P2OS): Revolutionizing Operating Systems and Human-Computer Interaction with Integrated AI Generative Models
Gabriele Tolomei, Cesare Campagnano, Fabrizio Silvestri +1
In this paper, we present a groundbreaking paradigm for human-computer interaction that revolutionizes the traditional notion of an operating system. Within this innovative framewo…
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…
Fauno: The Italian Large Language Model that will leave you senza parole!
Andrea Bacciu, Giovanni Trappolini, Andrea Santilli +2
This paper presents Fauno, the first and largest open-source Italian conversational Large Language Model (LLM). Our goal with Fauno is to democratize the study of LLMs in Italian,…
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,…
Renormalized Graph Representations for Node Classification
Francesco Caso, Giovanni Trappolini, Andrea Bacciu +2
Graph neural networks process information on graphs represented at a given resolution scale. We analyze the effect of using different coarse-grained graph resolutions, obtained thr…
The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples
Ziheng Chen, Fabrizio Silvestri, Jia Wang +2
Deep learning-based recommender systems have become an integral part of several online platforms. However, their black-box nature emphasizes the need for explainable artificial int…
Attention-likelihood relationship in transformers
Valeria Ruscio, Valentino Maiorca, Fabrizio Silvestri
We analyze how large language models (LLMs) represent out-of-context words, investigating their reliance on the given context to capture their semantics. Our likelihood-guided text…
Learning with Noisy Labels through Learnable Weighting and Centroid Similarity
Farooq Ahmad Wani, Maria Sofia Bucarelli, Fabrizio Silvestri
We introduce a novel method for training machine learning models in the presence of noisy labels, which are prevalent in domains such as medical diagnosis and autonomous driving an…