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 PoddaPostDoc Researcher, University of PisaVerified email at di.unipi.it
Alessandro SperdutiUniversity of PadovaVerified email at math.unipd.it
Vincenzo LomonacoAssistant Professor @ Unipi, Co-Founding President & Lab Director @ ContinualAI.orgVerified email at 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
Maurizio Di RoccoPostdoc Örebro University, AASS centerVerified email at aass.oru.se
Claudio VairoResearcher at ISTI-CNRVerified email at isti.cnr.it
Claudio GennaroISTI-CNR, PisaVerified email at isti.cnr.it
Daniele CastellanaPhD student at University di PisaVerified email at di.unipi.it
Giuseppe AmatoDirector of Research - ISTI - Consiglio Nazionale delle RicercheVerified email at isti.cnr.it
Paolo BarsocchiSenior Researcher @ ISTI-CNRVerified email at isti.cnr.it
Francesco CrecchiPhD student in Computer Science, University of PisaVerified email at di.unipi.it
Sonya ColemanUniversity of UlsterVerified email at ulster.ac.uk
Federico VozziInstitute of Clinical Physiology - CNRVerified email at ifc.cnr.it
Filippo PalumboInformation Science and Technologies Institute ISTI - CNRVerified email at isti.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	331	2019
A gentle introduction to deep learning for graphs D Bacciu, F Errica, A Micheli, M Podda Neural Networks 129, 203-221, 2020	199	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	126	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	104	2021
Internet of robotic things: converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms O Vermesan, A Bröring, E Tragos, M Serrano, D Bacciu, S Chessa, ... River Publishers, 2017	103	2017
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	81	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	67	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	61	2018
Continual Learning for Recurrent Neural Networks: an Empirical Evaluation A Cossu, A Carta, V Lomonaco, D Bacciu Neural Networks 143, 607-627, 2021	50	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	44	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	44	2012
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	35	2014
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	33	2015
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	32	2017
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	30	2017
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	30	2011
Efficient identification of independence networks using mutual information D Bacciu, TA Etchells, PJG Lisboa, J Whittaker Computational Statistics 28, 621-646, 2013	28	2013
Generative kernels for tree-structured data D Bacciu, A Micheli, A Sperduti IEEE transactions on neural networks and learning systems 29 (10), 4932-4946, 2018	27	2018
Edge-based sequential graph generation with recurrent neural networks D Bacciu, A Micheli, M Podda Neurocomputing 416, 177-189, 2020	25	2020

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