Davide Bacciu

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Alessio MicheliDepartment of Computer Science, University of PisaVerified email at di.unipi.it
Claudio GallicchioTenure Track Assistant Professor at the Department of Computer Science, University of PisaVerified email at di.unipi.it
Antonio CartaAssistant Professor @ Università di PisaVerified email at unipi.it
Federico ErricaResearch Scientist, NEC Laboratories EuropeVerified email at neclab.eu
Stefano ChessaFull Professor, Department of Computer Science, University of PisaVerified email at di.unipi.it
Marco PoddaAssistant Professor, University of PisaVerified email at unipi.it
Alessandro SperdutiUniversity of PadovaVerified email at math.unipd.it
Vincenzo LomonacoAssistant Professor @ Unipi, Co-Founding President @ ContinualAI.orgVerified email at unipi.it
Andrea Cossupost-doc researcher, University of PisaVerified email at di.unipi.it
Alessandro SaffiottiProfessor of Computer Science, University or Örebro, SwedenVerified email at aass.oru.se
Mauro DragoneAssociate Professor, Heriot-Watt University, Edinburgh, Scotland, UKVerified email at hw.ac.uk
Claudio VairoResearcher at ISTI-CNRVerified email at isti.cnr.it
Claudio GennaroISTI-CNR, PisaVerified email at isti.cnr.it
Maurizio Di RoccoPostdoc Örebro University, AASS centerVerified email at aass.oru.se
Giuseppe AmatoDirector of Research - ISTI - Consiglio Nazionale delle RicercheVerified email at isti.cnr.it
Daniele CastellanaPhD student at University di PisaVerified email at di.unipi.it
Sonya ColemanUniversity of UlsterVerified email at ulster.ac.uk
Paolo BarsocchiSenior Researcher @ ISTI-CNRVerified email at isti.cnr.it
Francesco CrecchiPhD student in Computer Science, University of PisaVerified email at di.unipi.it
Federico VozziInstitute of Clinical Physiology - CNRVerified email at ifc.cnr.it

Davide Bacciu

Dipartimento di Informatica, Università di Pisa

Verified email at di.unipi.it - Homepage

Deep Learning Generative Models Learning for Graphs Continual Learning Pervasive AI


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A fair comparison of graph neural networks for graph classification F Errica, M Podda, D Bacciu, A Micheli Proceedings of the 8th International Conference on Learning Representations …, 2019	376	2019
A gentle introduction to deep learning for graphs D Bacciu, F Errica, A Micheli, M Podda Neural Networks 129, 203-221, 2020	221	2020
An experimental characterization of reservoir computing in ambient assisted living applications D Bacciu, P Barsocchi, S Chessa, C Gallicchio, A Micheli Neural Computing and Applications 24, 1451-1464, 2014	125	2014
Avalanche: an end-to-end library for continual learning V Lomonaco, L Pellegrini, A Cossu, A Carta, G Graffieti, TL Hayes, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021	124	2021
Internet of robotic things–converging sensing/actuating, hyperconnectivity, artificial intelligence and IoT platforms O Vermesan, A Bröring, E Tragos, M Serrano, D Bacciu, S Chessa, ... Cognitive Hyperconnected Digital Transformation, 97-155, 2022	106	2022
Contextual graph markov model: A deep and generative approach to graph processing D Bacciu, F Errica, A Micheli International conference on machine learning, 294-303, 2018	88	2018
Learning from humans how to grasp: a data-driven architecture for autonomous grasping with anthropomorphic soft hands C Della Santina, V Arapi, G Averta, F Damiani, G Fiore, A Settimi, ... IEEE Robotics and Automation Letters 4 (2), 1533-1540, 2019	76	2019
A machine learning approach to estimating preterm infants survival: development of the Preterm Infants Survival Assessment (PISA) predictor M Podda, D Bacciu, A Micheli, R Bellù, G Placidi, L Gagliardi Scientific reports 8 (1), 13743, 2018	67	2018
Continual Learning for Recurrent Neural Networks: an Empirical Evaluation A Cossu, A Carta, V Lomonaco, D Bacciu Neural Networks 143, 607-627, 2021	60	2021
Robotic ubiquitous cognitive ecology for smart homes G Amato, D Bacciu, M Broxvall, S Chessa, S Coleman, M Di Rocco, ... Journal of Intelligent & Robotic Systems 80, 57-81, 2015	48	2015
A deep generative model for fragment-based molecule generation M Podda, D Bacciu, A Micheli International Conference on Artificial Intelligence and Statistics, 2240-2250, 2020	45	2020
Compositional Generative Mapping for Tree-Structured Data - Part I: Bottom-Up Probabilistic Modeling of Trees D Bacciu, A Micheli, A Sperduti IEEE Transactions on Neural Networks and Learning Systems 23 (12), 1987 - 2002, 2012	45	2012
An experience in using machine learning for short-term predictions in smart transportation systems D Bacciu, A Carta, S Gnesi, L Semini Journal of Logical and Algebraic Methods in Programming 87, 52-66, 2017	33	2017
Learning context-aware mobile robot navigation in home environments D Bacciu, C Gallicchio, A Micheli, M Di Rocco, A Saffiotti IISA 2014, the 5th international conference on information, intelligence …, 2014	33	2014
A learning system for automatic Berg Balance Scale score estimation D Bacciu, S Chessa, C Gallicchio, A Micheli, L Pedrelli, E Ferro, ... Engineering Applications of Artificial Intelligence 66, 60-74, 2017	32	2017
A cognitive robotic ecology approach to self-configuring and evolving AAL systems M Dragone, G Amato, D Bacciu, S Chessa, S Coleman, M Di Rocco, ... Engineering Applications of Artificial Intelligence 45, 269-280, 2015	32	2015
Efficient identification of independence networks using mutual information D Bacciu, TA Etchells, PJG Lisboa, J Whittaker Computational Statistics 28, 621-646, 2013	30	2013
Predicting user movements in heterogeneous indoor environments by reservoir computing D Bacciu, C Gallicchio, A Micheli, S Chessa, P Barsocchi Proc. of the IJCAI Workshop on Space, Time and Ambient Intelligence (STAMI …, 2011	29	2011
Edge-based sequential graph generation with recurrent neural networks D Bacciu, A Micheli, M Podda Neurocomputing 416, 177-189, 2020	26	2020
Detecting adversarial examples through nonlinear dimensionality reduction F Crecchi, D Bacciu, B Biggio In 27th European Symposium on Artificial Neural Networks, Computational …, 2019	26	2019

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