Here you can find a consolidated (a.k.a. slowly updated) list of my publications. A frequently updated (and possibly noisy) list of works is available on my Google Scholar profile.
Please find below a short list of highlight publications for my recent activity.
Davide, Bacciu; Michele, Colombo; Davide, Morelli; David, Plans
Randomized neural networks for preference learning with physiological data Journal Article
In: Neurocomputing, vol. 298, pp. 9-20, 2018.
@article{neurocomp2017,
title = {Randomized neural networks for preference learning with physiological data},
author = {Bacciu Davide and Colombo Michele and Morelli Davide and Plans David},
editor = {Fabio Aiolli and Luca Oneto and Michael Biehl },
url = {https://authors.elsevier.com/a/1Wxbz_L2Otpsb3},
doi = {10.1016/j.neucom.2017.11.070},
year = {2018},
date = {2018-07-12},
journal = {Neurocomputing},
volume = {298},
pages = {9-20},
abstract = {The paper discusses the use of randomized neural networks to learn a complete ordering between samples of heart-rate variability data by relying solely on partial and subject-dependent information concerning pairwise relations between samples. We confront two approaches, i.e. Extreme Learning Machines and Echo State Networks, assessing the effectiveness in exploiting hand-engineered heart-rate variability features versus using raw beat-to-beat sequential data. Additionally, we introduce a weight sharing architecture and a preference learning error function whose performance is compared with a standard architecture realizing pairwise ranking as a binary-classification task. The models are evaluated on real-world data from a mobile application realizing a guided breathing exercise, using a dataset of over 54K exercising sessions. Results show how a randomized neural model processing information in its raw sequential form can outperform its vectorial counterpart, increasing accuracy in predicting the correct sample ordering by about 20%. Further, the experiments highlight the importance of using weight sharing architectures to learn smooth and generalizable complete orders induced by the preference relation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The paper discusses the use of randomized neural networks to learn a complete ordering between samples of heart-rate variability data by relying solely on partial and subject-dependent information concerning pairwise relations between samples. We confront two approaches, i.e. Extreme Learning Machines and Echo State Networks, assessing the effectiveness in exploiting hand-engineered heart-rate variability features versus using raw beat-to-beat sequential data. Additionally, we introduce a weight sharing architecture and a preference learning error function whose performance is compared with a standard architecture realizing pairwise ranking as a binary-classification task. The models are evaluated on real-world data from a mobile application realizing a guided breathing exercise, using a dataset of over 54K exercising sessions. Results show how a randomized neural model processing information in its raw sequential form can outperform its vectorial counterpart, increasing accuracy in predicting the correct sample ordering by about 20%. Further, the experiments highlight the importance of using weight sharing architectures to learn smooth and generalizable complete orders induced by the preference relation.
Davide, Bacciu; Michele, Colombo; Davide, Morelli; David, Plans
ELM Preference Learning for Physiological Data Conference
Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'17), i6doc.com, Louvain-la-Neuve, Belgium, 2017, ISBN: 978-2-875870384.
@conference{esann2017,
title = {ELM Preference Learning for Physiological Data},
author = {Bacciu Davide and Colombo Michele and Morelli Davide and Plans David},
editor = {Michel Verleysen},
isbn = {978-2-875870384},
year = {2017},
date = {2017-04-28},
urldate = {2017-04-28},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'17)},
pages = {99-104},
publisher = {i6doc.com},
address = {Louvain-la-Neuve, Belgium},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}