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.
Bacciu, Davide; Sarli, Daniele Di; Faraji, Pouria; Gallicchio, Claudio; Micheli, Alessio Federated Reservoir Computing Neural Networks Conference Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021), IEEE, 2021. 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. 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{BacciuIJCNN2021,
title = {Federated Reservoir Computing Neural Networks},
author = {Davide Bacciu and Daniele Di Sarli and Pouria Faraji and Claudio Gallicchio and Alessio Micheli},
doi = {10.1109/IJCNN52387.2021.9534035},
year = {2021},
date = {2021-07-18},
urldate = {2021-07-18},
booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021)},
publisher = {IEEE},
abstract = {A critical aspect in Federated Learning is the aggregation strategy for the combination of multiple models, trained on the edge, into a single model that incorporates all the knowledge in the federation. Common Federated Learning approaches for Recurrent Neural Networks (RNNs) do not provide guarantees on the predictive performance of the aggregated model. In this paper we show how the use of Echo State Networks (ESNs), which are efficient state-of-the-art RNN models for time-series processing, enables a form of federation that is optimal in the sense that it produces models mathematically equivalent to the corresponding centralized model. Furthermore, the proposed method is compliant with privacy constraints. The proposed method, which we denote as Incremental Federated Learning, is experimentally evaluated against an averaging strategy on two datasets for human state and activity recognition.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@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}
}
@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}
}