Výsledky vyhledávání - "d'Aloisio, Giordano"

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Exploring LLM-Driven Explanations for Quantum Algorithms

Autor: d'Aloisio, Giordano, Fortz, Sophie, Hanna, Carol, Fortunato, Daniel, Bensoussan, Avner, Usandizaga, Eñaut Mendiluze, Sarro, Federica

Background: Quantum computing is a rapidly growing new programming paradigm that brings significant changes to the design and implementation of algorithms. Understanding quantum algorithms requires knowledge of physics and mathematics, which can be c

Externí odkaz: http://arxiv.org/abs/2409.19028

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GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image Generation

Autor: Gong, Jingzhi, Li, Sisi, d'Aloisio, Giordano, Ding, Zishuo, Ye, Yulong, Langdon, William B., Sarro, Federica

Tuning the parameters and prompts for improving AI-based text-to-image generation has remained a substantial yet unaddressed challenge. Hence we introduce GreenStableYolo, which improves the parameters and prompts for Stable Diffusion to both reduce

Externí odkaz: http://arxiv.org/abs/2407.14982

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How fair are we? From conceptualization to automated assessment of fairness definitions

Autor: d'Aloisio, Giordano, Di Sipio, Claudio, Di Marco, Antinisca, Di Ruscio, Davide

Fairness is a critical concept in ethics and social domains, but it is also a challenging property to engineer in software systems. With the increasing use of machine learning in software systems, researchers have been developing techniques to automa

Externí odkaz: http://arxiv.org/abs/2404.09919

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Data-Driven Analysis of Gender Fairness in the Software Engineering Academic Landscape

Autor: d'Aloisio, Giordano, D'Angelo, Andrea, Marzi, Francesca, Di Marco, Diana, Stilo, Giovanni, Di Marco, Antinisca

Gender bias in education gained considerable relevance in the literature over the years. However, while the problem of gender bias in education has been widely addressed from a student perspective, it is still not fully analysed from an academic poin

Externí odkaz: http://arxiv.org/abs/2309.11239

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Towards a Prediction of Machine Learning Training Time to Support Continuous Learning Systems Development

Autor: Marzi, Francesca, d'Aloisio, Giordano, Di Marco, Antinisca, Stilo, Giovanni

The problem of predicting the training time of machine learning (ML) models has become extremely relevant in the scientific community. Being able to predict a priori the training time of an ML model would enable the automatic selection of the best mo

Externí odkaz: http://arxiv.org/abs/2309.11226

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A Decision Tree to Shepherd Scientists through Data Retrievability

Autor: Bianchi, Andrea, d'Aloisio, Giordano, Marzi, Francesca, Di Marco, Antinisca

Publikováno v: Second Workshop on Reproducibility and Replication of Research Results (RRRR 2023)

Reproducibility is a crucial aspect of scientific research that involves the ability to independently replicate experimental results by analysing the same data or repeating the same experiment. Over the years, many works have been proposed to make th

Externí odkaz: http://arxiv.org/abs/2304.05767

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Modeling Quality and Machine Learning Pipelines through Extended Feature Models

Autor: d'Aloisio, Giordano, Di Marco, Antinisca, Stilo, Giovanni

The recently increased complexity of Machine Learning (ML) methods, led to the necessity to lighten both the research and industry development processes. ML pipelines have become an essential tool for experts of many domains, data scientists and rese

Externí odkaz: http://arxiv.org/abs/2207.07528

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