| Neural network for graphs: A contextual constructive approach A Micheli IEEE Transactions on Neural Networks 20 (3), 498-511, 2009 | 813 | 2009 |
| A fair comparison of graph neural networks for graph classification F Errica, M Podda, D Bacciu, A Micheli 8th International Conference on Learning Representations (ICLR 2020), 2020 | 572 | 2020 |
| Deep reservoir computing: A critical experimental analysis C Gallicchio, A Micheli, L Pedrelli Neurocomputing 268, 87-99, 2017 | 554 | 2017 |
| A gentle introduction to deep learning for graphs D Bacciu, F Errica, A Micheli, M Podda Neural Networks 129, 203-221, 2020 | 360 | 2020 |
| Design of deep echo state networks C Gallicchio, A Micheli, L Pedrelli Neural Networks 108, 33-47, 2018 | 291 | 2018 |
| Graph echo state networks C Gallicchio, A Micheli The 2010 international joint conference on neural networks (IJCNN), 1-8, 2010 | 290 | 2010 |
| Architectural and markovian factors of echo state networks C Gallicchio, A Micheli Neural Networks 24 (5), 440-456, 2011 | 213 | 2011 |
| 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 | 185 | 2016 |
| Recursive self-organizing network models B Hammer, A Micheli, A Sperduti, M Strickert Neural Networks 17 (8-9), 1061-1085, 2004 | 173 | 2004 |
| Echo state property of deep reservoir computing networks C Gallicchio, A Micheli Cognitive Computation 9, 337-350, 2017 | 168 | 2017 |
| 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 | 150 | 2022 |
| 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 | 145 | 2014 |
| Deep echo state network (deepesn): A brief survey C Gallicchio, A Micheli arXiv preprint arXiv:1712.04323, 2017 | 136 | 2017 |
| A general framework for unsupervised processing of structured data B Hammer, A Micheli, A Sperduti, M Strickert Neurocomputing 57, 3-35, 2004 | 135 | 2004 |
| Fast and deep graph neural networks C Gallicchio, A Micheli Proceedings of the AAAI conference on artificial intelligence 34 (04), 3898-3905, 2020 | 128 | 2020 |
| Application of cascade correlation networks for structures to chemistry AM Bianucci, A Micheli, A Sperduti, A Starita Applied Intelligence 12, 117-147, 2000 | 123 | 2000 |
| 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 | 104 | 2001 |
| 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 | 100 | 2018 |
| 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 | 92 | 2018 |
| Tree echo state networks C Gallicchio, A Micheli Neurocomputing 101, 319-337, 2013 | 89 | 2013 |
Claudio GallicchioAssociate Professor at the Department of Computer Science, University of PisaDirección de correo verificada de unipi.it
