S. Wang, Y. Li, D. Wang, W. Zhang, X. Chen, D. Dong, S. Wang, X. Zhang, P. Lin, C. Gallicchio, X. Xu, Q. Liu, K.-T. Cheng, Z. Wang, D. Shang, and M. Liu
Echo state graph neural networks with analogue random resistive memory arrays Nature Machine Intelligence, 5, 104-113, 2023

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A. Micheli and D. Tortorella
Discrete-time dynamic graph echo state networks Neurocomputing, 496, 85-95, 2022

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F. M. Bianchi, C. Gallicchio, and A. Micheli
Pyramidal reservoir graph neural network Neurocomputing, 470, 389-404, 2022

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C. Gallicchio and A. Micheli
Architectural richness in deep reservoir computing Neural Computing and Applications, 2022

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D. Castellana, F. Errica, D. Bacciu, and A. Micheli
The Infinite Contextual Graph Markov Model Proceedings of the 39th International Conference on Machine Learning (ICML 2022), 162, 2721-2737, 2022

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L. Oneto, N. Navarin, B. Biggio, F. Errica, A. Micheli, F. Scarselli, M. Bianchini, L. Demetrio, P. Bongini, A. Tacchella, and A. Sperduti
Towards learning trustworthily, automatically, and with guarantees on graphs: An overview Neurocomputing, 493, 217-243, 2022

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F. Errica, D. Bacciu, and A. Micheli
Graph Mixture Density Networks Proceedings of the 38th International Conference on Machine Learning (ICML 2021), 139, 3025-3035, 2021

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C. Gallicchio and A. Micheli
Fast and Deep Graph Neural Networks Proceedings of the 34th AAAI Conference, 2020

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M. Podda, D. Bacciu, and A. Micheli
A Deep Generative Model for Fragment-Based Molecule Generation Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR, 2020

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P. Bove., A. Micheli., P. Milazzo., and M. Podda.
Prediction of Dynamical Properties of Biochemical Pathways with Graph Neural Networks Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (Best Paper Award), SciTePress, 2020, ISBN 978-989-758-398-8

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D. Bacciu, F. Errica, and A. Micheli
Probabilistic Learning on Graphs via Contextual Architectures Journal of Machine Learning Research, 21(134), 1-39, 2020

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D. Bacciu, F. Errica, A. Micheli, and M. Podda
A Gentle Introduction to Deep Learning for Graphs Neural Networks, 129, 203-221, 2020

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F. Errica, M. Podda, D. Bacciu, and A. Micheli
A Fair Comparison of Graph Neural Networks for Graph Classification Proceedings of the 8th International Conference on Learning Representations (ICLR), 2020

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C. Gallicchio and A. Micheli
Deep Reservoir Neural Networks for Trees Information Sciences, 480, 174-193, 2019

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D. Bacciu and F. Crecchi
Augmenting Recurrent Neural Networks Resilience by Dropout IEEE Transactions on Neural Networks and Learning Systems, 1-7, 2019

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D. Bacciu and D. Castellana
Bayesian Mixtures of Hidden Tree Markov Models for Structured Data Clustering Neurocomputing, 342, 49-59, 2019

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D. Bacciu, F. Errica, and A. Micheli
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing Proceedings of the 35th International Conference on Machine Learning (ICML), 2018, ISBN 9781510867963

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C. Gallicchio and A. Micheli
Deep Tree Echo State Networks Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE, 2018, ISBN 9781509060146

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C. Gallicchio, A. Micheli, and L. Silvestri
Local Lyapunov exponents of deep echo state networks Neurocomputing, 298, 34-45, 2018

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M. Podda, D. Bacciu, A. Micheli, R. Bellù, G. Placidi, and L. Gagliardi
A machine learning approach to estimating preterm infants survival: development of the Preterm Infants Survival Assessment (PISA) predictor Scientific Reports, 8, 2018

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C. Gallicchio and A. Micheli
Deep Echo State Network (DeepESN): A Brief Survey Technical Report, 2018

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C. Gallicchio, A. Micheli, and L. Pedrelli
Design of deep echo state networks Neural Networks, 108, 33-47, 2018

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D. Bacciu, A. Micheli, and A. Sperduti
Generative Kernels for Tree-Structured Data IEEE Transactions on Neural Networks and Learning Systems, 29, 4932-4946, 2018

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C. Gallicchio, A. Micheli, and L. Pedrelli
Deep Reservoir Computing: A Critical Experimental Analysis Neurocomputing, 268, 87-99, 2017

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C. Gallicchio, J. D. Martin-Guerrero, A. Micheli, and E. Soria-Olivas
Randomized Machine Learning Approaches: Recent Developments and Challenges Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN), 2017, ISBN 978-287587038-4

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C. Gallicchio and A. Micheli
Echo State Property of Deep Reservoir Computing Networks Cognitive Computation, 9, 337-350, 2017

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F. Palumbo, C. Gallicchio, R. Pucci, and A. Micheli
Human Activity Recognition using Multisensor Data Fusion based on Reservoir Computing Ambient Intelligence and Smart Environments, 8, 87-107, 2016

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R. Barbuti, S. Chessa, A. Micheli, and R. Pucci
Localizing Tortoise Nests by Neural Networks Plos One, 11, 1-27, 2016

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D. Bacciu, C. Gallicchio, and A. Micheli
A reservoir activation kernel for trees Proceedings of the 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2016, ISBN 978-287587027-8

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C. Gallicchio and A. Micheli
Deep reservoir computing: A critical analysis Proceedings of the 24th European Symposium on Artificial Neural Networks (ESANN), 2016, ISBN 9782875870278

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M. Dragone, G. Amato, D. Bacciu, S. Chessa, S. Coleman, M. D. Rocco, C. Gallicchio, C. Gennaro, H. Lozano-Peiteado, L. Maguire, M. T. Mcginnity, A. Micheli, G. M. P. O׳Hare, A. Renteria, A. Saffiotti, C. F. Vairo, and P. Vance
A cognitive robotic ecology approach to self-configuring and evolving AAL systems Engineering Applications of Artificial Intelligence, 45, 269-280, 2015

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E. Crisostomi, C. Gallicchio, A. Micheli, M. Raugi, and M. Tucci
Prediction of the Italian electricity price for smart grid applications Neurocomputing, 170, 286-295, 2015

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G. Amato, D. Bacciu, M. Broxvall, S. Chessa, S. Coleman, M. D. Rocco, M. Dragone, C. Gallicchio, C. Gennaro, H. Lozano-Peiteado, M. T. Mcginnity, A. Micheli, A. Ray, A. Renteria, A. Saffiotti, D. Swords, C. F. Vairo, and P. Vance
Robotic Ubiquitous Cognitive Ecology for Smart Homes Intelligent & Robotic Systems, 80, 57-81, 2015

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D. Bacciu, P. Barsocchi, S. Chessa, C. Gallicchio, and A. Micheli
An experimental characterization of reservoir computing in ambient assisted living applications Neural Computing & Applications, 24, 1451-1464, 2014

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D. Bacciu, A. Micheli, and A. Sperduti
Compositional Generative Mapping for Tree-Structured Data - Part II: Topographic Projection Model IEEE Transactions on Neural Networks and Learning Systems, 24, 231-247, 2013

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C. Gallicchio and A. Micheli
Tree Echo State Networks Neurocomputing, 101, 319-337, 2013

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D. Bacciu, A. Micheli, and A. Sperduti
Compositional Generative Mapping for Tree-Structured Data - Part I: Bottom-Up Probabilistic Modeling of Trees. IEEE Transactions on Neural Networks and Learning Systems, 23, 1987-2002, 2012

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C. Gallicchio and A. Micheli
Architectural and Markovian factors of echo state networks Neural Networks, 24, 440-456, 2011

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C. G. Bertinetto, C. Duce, A. Micheli, R. Solaro, and M. R. Tiné
QSPR analysis of copolymers by recursive neural networks: Prediction of the glass transition temperature of (meth)acrylic random copolymers Molecular Informatics, 29, 635-643, 2010

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F. Masulli, A. Micheli, and A. Sperduti
Computational Intelligence and Bioengineering Frontiers in Artificial Intelligence and Applications, 196, 0-213, 2009

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C. Bertinetto, C. Duce, A. Micheli, R. Solaro, A. Starita, and M. R. Tiné
Evaluation of hierarchical structured representations for QSPR studies of small molecules and polymers by recursive neural networks Molecular Graphics and Modelling, 27, 797-802, 2009

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A. Micheli
Neural Network for Graphs: A Contextual Constructive Approach IEEE Transactions on Neural Networks, 20, 498-511, 2009

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D. Bacciu and A. Starita
Competitive Repetition Suppression (CoRe) Clustering: A Biologically Inspired Learning Model With Application to Robust Clustering IEEE Transactions on Neural Networks, 19, 1922-1941, 2008

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L. Bernazzani, C. Duce, A. Micheli, V. Mollica, A. Sperduti, A. Starita, and R. T. M.
Predicting Physical-Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks Chemical Information and Modeling, 46, 2030-2042, 2006

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B. Hammer, A. Micheli, and A. Sperduti
Universal Approximation Capability of Cascade Correlation for Structures Neural Computation, 17, 1109-1159, 2005

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B. Hammer, A. Micheli, A. Sperduti, and M. Strickert
Recursive Self-organizing Network Models Neural Networks, 17, 1061-1085, 2004

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A. M. Bianucci, A. Micheli, A. Sperduti, and A. Starita
A Novel Approach to QSPR/QSAR Based on Neural Networks for Structures Soft Computing Approaches in Chemistry, Springer-Verlag, 2003, ISBN 3540002456

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A. Micheli, A. Sperduti, A. Starita, and A. M. Bianucci
Analysis of the Internal Representations Developed By Neural Networks for Structures Applied to Quantitative Structure-Activity Relationship Studies of Benzodiazepines Chemical Information and Computer Sciences, 41, 202-218, 2001

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A. M. Bianucci, A. Micheli, A. Sperduti, and A. Starita
Application of Cascade Correlation Networks for Structures to Chemistry Applied Intelligence, 12, 117-146, 2000

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