| Neural network for graphs: A contextual constructive approach A Micheli IEEE Transactions on Neural Networks 20 (3), 498-511, 2009 | 168 | 2009 |
| Recursive self-organizing network models B Hammer, A Micheli, A Sperduti, M Strickert Neural Networks 17 (8-9), 1061-1085, 2004 | 151 | 2004 |
| Deep reservoir computing: A critical experimental analysis C Gallicchio, A Micheli, L Pedrelli Neurocomputing 268, 87-99, 2017 | 133 | 2017 |
| Architectural and markovian factors of echo state networks C Gallicchio, A Micheli Neural Networks 24 (5), 440-456, 2011 | 127 | 2011 |
| A general framework for unsupervised processing of structured data B Hammer, A Micheli, A Sperduti, M Strickert Neurocomputing 57, 3-35, 2004 | 121 | 2004 |
| Application of cascade correlation networks for structures to chemistry AM Bianucci, A Micheli, A Sperduti, A Starita Applied Intelligence 12 (1-2), 117-147, 2000 | 104 | 2000 |
| 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 (6), 1451-1464, 2014 | 94 | 2014 |
| Analysis of the internal representations developed by neural networks for structures applied to quantitative structure− activity relationship studies of benzodiazepines A Micheli, A Sperduti, A Starita, AM Bianucci Journal of Chemical Information and Computer Sciences 41 (1), 202-218, 2001 | 90 | 2001 |
| Human activity recognition using multisensor data fusion based on reservoir computing F Palumbo, C Gallicchio, R Pucci, A Micheli Journal of Ambient Intelligence and Smart Environments 8 (2), 87-107, 2016 | 83 | 2016 |
| Echo state property of deep reservoir computing networks C Gallicchio, A Micheli Cognitive Computation 9 (3), 337-350, 2017 | 66 | 2017 |
| Tree echo state networks C Gallicchio, A Micheli Neurocomputing 101, 319-337, 2013 | 61 | 2013 |
| Graph echo state networks C Gallicchio, A Micheli The 2010 International Joint Conference on Neural Networks (IJCNN), 1-8, 2010 | 61* | 2010 |
| Ionic liquids: prediction of their melting points by a recursive neural network model R Bini, C Chiappe, C Duce, A Micheli, R Solaro, A Starita, MR Tiné Green Chemistry 10 (3), 306-309, 2008 | 61 | 2008 |
| Contextual processing of structured data by recursive cascade correlation A Micheli, D Sona, A Sperduti IEEE Transactions on Neural Networks 15 (6), 1396-1410, 2004 | 61 | 2004 |
| Design of deep echo state networks C Gallicchio, A Micheli, L Pedrelli Neural Networks 108, 33-47, 2018 | 57 | 2018 |
| A fair comparison of graph neural networks for graph classification F Errica, M Podda, D Bacciu, A Micheli arXiv preprint arXiv:1912.09893, 2019 | 54 | 2019 |
| Universal approximation capability of cascade correlation for structures B Hammer, A Micheli, A Sperduti Neural Computation 17 (5), 1109-1159, 2005 | 51 | 2005 |
| Predicting Physical− Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks L Bernazzani, C Duce, A Micheli, V Mollica, A Sperduti, A Starita, MR Tiné Journal of chemical information and modeling 46 (5), 2030-2042, 2006 | 49 | 2006 |
| 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 | 48 | 2017 |
| Prediction of the glass transition temperature of (meth) acrylic polymers containing phenyl groups by recursive neural network C Bertinetto, C Duce, A Micheli, R Solaro, A Starita, MR Tiné Polymer 48 (24), 7121-7129, 2007 | 46 | 2007 |
Claudio GallicchioAssistant Professor at the Department of Computer Science, University of PisaEmail verificata su di.unipi.it
