Zobrazeno 1 - 10
of 11 970
pro vyhledávání: '"A. Passerini"'
Publikováno v:
Communications Physics, Vol 7, Iss 1, Pp 1-14 (2024)
Abstract Temporal networks are essential for modeling and understanding time-dependent systems, from social interactions to biological systems. However, real-world data to construct meaningful temporal networks are expensive to collect or unshareable
Externí odkaz:
https://doaj.org/article/230ca23ac7854dbdb4f486f673976606
Graph Neural Networks (GNNs) have emerged as the predominant paradigm for learning from graph-structured data, offering a wide range of applications from social network analysis to bioinformatics. Despite their versatility, GNNs face challenges such
Externí odkaz:
http://arxiv.org/abs/2407.20067
As Graph Neural Networks (GNNs) become more pervasive, it becomes paramount to build robust tools for computing explanations of their predictions. A key desideratum is that these explanations are faithful, i.e., that they portray an accurate picture
Externí odkaz:
http://arxiv.org/abs/2406.15156
Autor:
Bortolotti, Samuele, Marconato, Emanuele, Carraro, Tommaso, Morettin, Paolo, van Krieken, Emile, Vergari, Antonio, Teso, Stefano, Passerini, Andrea
The advent of powerful neural classifiers has increased interest in problems that require both learning and reasoning. These problems are critical for understanding important properties of models, such as trustworthiness, generalization, interpretabi
Externí odkaz:
http://arxiv.org/abs/2406.10368
Autor:
Ahmed, Kareem, Teso, Stefano, Morettin, Paolo, Di Liello, Luca, Ardino, Pierfrancesco, Gobbi, Jacopo, Liang, Yitao, Wang, Eric, Chang, Kai-Wei, Passerini, Andrea, Broeck, Guy Van den
Structured output prediction problems are ubiquitous in machine learning. The prominent approach leverages neural networks as powerful feature extractors, otherwise assuming the independence of the outputs. These outputs, however, jointly encode an o
Externí odkaz:
http://arxiv.org/abs/2405.07387
Autor:
Esfahani, Seyedehdelaram, De Toni, Giovanni, Lepri, Bruno, Passerini, Andrea, Tentori, Katya, Zancanaro, Massimo
Algorithmic Recourse aims to provide actionable explanations, or recourse plans, to overturn potentially unfavourable decisions taken by automated machine learning models. In this paper, we propose an interaction paradigm based on a guided interactio
Externí odkaz:
http://arxiv.org/abs/2404.05270
Large Language Models (LLMs) demonstrate an impressive capacity to recall a vast range of factual knowledge. However, understanding their underlying reasoning and internal mechanisms in exploiting this knowledge remains a key research area. This work
Externí odkaz:
http://arxiv.org/abs/2404.03623
We explore the potential of Large Language Models (LLMs) to assist and potentially correct physicians in medical decision-making tasks. We evaluate several LLMs, including Meditron, Llama2, and Mistral, to analyze the ability of these models to inter
Externí odkaz:
http://arxiv.org/abs/2403.20288
There is increasing interest in developing AIs for assisting human decision-making in high-stakes tasks, such as medical diagnosis, for the purpose of improving decision quality and reducing cognitive strain. Mainstream approaches team up an expert w
Externí odkaz:
http://arxiv.org/abs/2403.16501
Autor:
Marconato, Emanuele, Bortolotti, Samuele, van Krieken, Emile, Vergari, Antonio, Passerini, Andrea, Teso, Stefano
Neuro-Symbolic (NeSy) predictors that conform to symbolic knowledge - encoding, e.g., safety constraints - can be affected by Reasoning Shortcuts (RSs): They learn concepts consistent with the symbolic knowledge by exploiting unintended semantics. RS
Externí odkaz:
http://arxiv.org/abs/2402.12240