@inproceedings{FraLamRig20-LOD-IC,
author = {Fraccaroli, Michele
and Lamma, Evelina
and Riguzzi, Fabrizio},
editor = {Nicosia, Giuseppe
and Ojha, Varun
and La Malfa, Emanuele
and Jansen, Giorgio
and Sciacca, Vincenzo
and Pardalos, Panos
and Giuffrida, Giovanni
and Umeton, Renato},
title = {Automatic Setting of {DNN} Hyper-Parameters by Mixing {Bayesian Optimization} and Tuning Rules},
booktitle = {Machine Learning, Optimization, and Data Science, 6th International Conference, LOD 2020, Siena, Italy, July 19–23, 2020, Revised Selected Papers, Part I},
year = {2020},
publisher = {Springer International Publishing},
address = {Cham},
pages = {477--488},
abstract = {Deep learning techniques play an increasingly important role in industrial and research environments due to their outstanding results. However, the large number of hyper-parameters to be set may lead to errors if they are set manually. The state-of-the-art hyper-parameters tuning methods are grid search, random search, and Bayesian Optimization. The first two methods are expensive because they try, respectively, all possible combinations and random combinations of hyper-parameters. Bayesian Optimization, instead, builds a surrogate model of the objective function, quantifies the uncertainty in the surrogate using Gaussian Process Regression and uses an acquisition function to decide where to sample the new set of hyper-parameters. This work faces the field of Hyper-Parameters Optimization (HPO). The aim is to improve Bayesian Optimization applied to Deep Neural Networks. For this goal, we build a new algorithm for evaluating and analyzing the results of the network on the training and validation sets and use a set of tuning rules to add new hyper-parameters and/or to reduce the hyper-parameter search space to select a better combination.},
isbn = {978-3-030-64583-0},
doi = {10.1007/978-3-030-64583-0_43},
note = {The final publication is available at Springer via \url{https://link.springer.com/chapter/10.1007/978-3-030-64583-0_43}},
copyright = {Springer},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/FraLamRig-LOD20.pdf},
series = {Lecture Notes in Computer Science},
volume = {12565}
}
@inproceedings{FraMazBiz21-ICTS4eHealth-IC,
author = {Michele Fraccaroli and Giulia Mazzucchelli and Alice Bizzarri},
title = {Machine Learning Techniques for Extracting Relevant Features from Clinical
Data for {COVID-19} Mortality Prediction},
booktitle = {2021 Symposium on Computers and Communications (ISCC): 26th IEEE Symposium
on Computers and Communications - Workshop on ICT Solutions for eHealth
(ICTS4eHealth) (ICTS4eHealth2021)},
address = {Athens, Greece},
days = 4,
month = sep,
year = 2021,
keywords = {Machine Learning; COVID-19; Health Informatics; Explanation; Predictive
Analysis},
abstract = {The role of Machine Learning (ML) in healthcare is based on
the ability of a machine to analyse the huge amounts of
data available for each patient, like age, medical history,
overall health status, test results, etc. With ML algorithms it is
possible to learn models from data for the early identification
of pathologies, and their severity. Early identification is crucial
to proceed as soon as possible with the necessary therapeutic
actions. This work applies modern ML techniques to clinical data
of either COVID-19 positive and COVID-19 negative patients
with pulmonary complications, to learn mortality prediction
models for both groups of patients, and compare results. We
have focused on symbolic methods for building
classifiers able to extract patterns from clinical data. This
approach leads to predictive Artificial Intelligence (AI) systems
working on medical data, and able to explain the reasons that
lead the systems themselves to reach a certain conclusion.},
pages = {1-7},
doi = {10.1109/ISCC53001.2021.9631477}
}
@inproceedings{FraLamRig22-recently-IC,
title = {Exploiting Parameters Learning for Hyper-parameters Optimization in Deep Neural Networks},
booktitle = {Proceedings of the 38th International Conference on Logic Programming (Technical Communications), Recently Published Research track},
issn = {2075-2180},
doi = {10.4204/EPTCS.364},
volume = {364},
pages = {142--144},
series = {Electronic Proceedings in Theoretical Computer Science},
publisher = {Open Publishing Association},
address = {Waterloo, Australia},
editor = {Yuliya Lierler and Jose F. Morales and Carmine Dodaro and Veronica Dahl and Martin Gebser and Tuncay Tekle},
year = {2022},
author = {Michele Fraccaroli and Fabrizio Riguzzi and Evelina Lamma},
url = {https://arxiv.org/html/2208.02685v1/#EPTCS364.17}
}
@article{BelBerBizFavFraZes23-MCSoC-IC,
author = {Bellodi, Elena and Bertozzi, Davide and Bizzarri, Alice and Favalli, Michele and Fraccaroli, Michele and Zese, Riccardo},
booktitle = {2023 IEEE 16th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)},
title = {Efficient Resource-Aware Neural Architecture Search with a Neuro-Symbolic Approach},
year = {2023},
volume = {},
number = {},
pages = {171-178},
keywords = {Deep learning;Performance evaluation;Costs;Terminology;Computational modeling;Search problems;Probabilistic logic;neural networks;neural network hardware accelerators;architecture search;probabilistic logic programming},
doi = {10.1109/MCSoC60832.2023.00034}
}
@inproceedings{AzzBizzFracBertLam23-CSCI-IC,
author = {Azzolini, Damiano and Bizzarri, Alice and Fraccaroli, Michele and Bertasi, Francesco and Lamma, Evelina},
booktitle = {2023 International Conference on Computational Science and Computational Intelligence (CSCI)},
title = {A Machine Learning Pipeline to Analyse Multispectral and Hyperspectral Images: Full/Regular Research Paper (CSCI-RTHI)},
year = {2023},
volume = {},
number = {},
pages = {1306-1311},
doi = {10.1109/CSCI62032.2023.00216}
}
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