Alessio Micheli
Cited by
Cited by
Recursive self-organizing network models
B Hammer, A Micheli, A Sperduti, M Strickert
Neural Networks 17 (8-9), 1061-1085, 2004
A general framework for unsupervised processing of structured data
B Hammer, A Micheli, A Sperduti, M Strickert
Neurocomputing 57, 3-35, 2004
Neural network for graphs: A contextual constructive approach
A Micheli
IEEE Transactions on Neural Networks 20 (3), 498-511, 2009
Architectural and markovian factors of echo state networks
C Gallicchio, A Micheli
Neural Networks 24 (5), 440-456, 2011
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
Deep reservoir computing: A critical experimental analysis
C Gallicchio, A Micheli, L Pedrelli
Neurocomputing 268, 87-99, 2017
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
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
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
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
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 TinÚ
Green Chemistry 10 (3), 306-309, 2008
Tree echo state networks
C Gallicchio, A Micheli
Neurocomputing 101, 319-337, 2013
Universal approximation capability of cascade correlation for structures
B Hammer, A Micheli, A Sperduti
Neural Computation 17 (5), 1109-1159, 2005
Echo state property of deep reservoir computing networks
C Gallicchio, A Micheli
Cognitive Computation 9 (3), 337-350, 2017
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 TinÚ
Journal of chemical information and modeling 46 (5), 2030-2042, 2006
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 TinÚ
Polymer 48 (24), 7121-7129, 2007
Graph echo state networks
C Gallicchio, A Micheli
The 2010 International Joint Conference on Neural Networks (IJCNN), 1-8, 2010
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 (1), 57-81, 2015
Prediction of polymer properties from their structure by recursive neural networks
C Duce, A Micheli, A Starita, MR TinÚ, R Solaro
Macromolecular rapid communications 27 (9), 711-715, 2006
Internet of robotic things: converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
O Vermesan, A Br÷ring, E Tragos, M Serrano, D Bacciu, S Chessa, ...
River Publishers, 2017
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