A. Micheli

Alessio Micheli, Alessandro Sperduti, Antonina Starita, 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

Discrete-time dynamic graph echo state networks

A. Micheli, Domenico Tortorella, 2022, Neurocomputing.

A machine learning approach to estimating preterm infants survival: development of the Preterm Infants Survival Assessment (PISA) predictor

Davide Bacciu, Alessio Micheli, Marco Podda, 2018, Scientific Reports.

Prediction of Dynamical Properties of Biochemical Pathways with Graph Neural Networks

Alessio Micheli, Paolo Milazzo, Pasquale Bove, 2020, BIOINFORMATICS.

On the effectiveness of Gated Echo State Networks for data exhibiting long-term dependencies

Daniele Di Sarli, A. Micheli, C. Gallicchio, 2022, Comput. Sci. Inf. Syst..

Complex Data: Learning Trustworthily, Automatically, and with Guarantees

Alessio Micheli, Franco Scarselli, Alessandro Sperduti, 2021, ESANN.

Recursive neural networks prediction of glass transition temperature from monomer structure: an application to acrylic and methacrylic polymers

Alessio Micheli, Antonina Starita, Maria Rosaria Tine, 2009 .

QSPR Analysis of Copolymers by Recursive Neural Networks: Prediction of the Glass Transition Temperature of (Meth)acrylic Random Copolymers

A. Micheli, R. Solaro, C. Duce, 2010, Molecular informatics.

Recursive Neural Networks for Quantitative Structure-Property Relationship Analysis of Polymers

A. Micheli, A. Starita, R. Solaro, 2005 .

Compositional generative mapping of structured data

Davide Bacciu, Alessio Micheli, Alessandro Sperduti, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

A Bottom-up Hidden Tree Markov Model

A. Micheli, A. Sperduti, D. Bacciu, 2010 .

A mathematical characterization of the architectural bias of recursive models

Alessio Micheli, Barbara Hammer, Peter Tino, 2004 .

Guest Editorial: Trends in Reservoir Computing

Claudio Gallicchio, Alessio Micheli, Simone Scardapane, 2021, Cognitive Computation.

ABSTRACT AUTHOR(S) AND TITLE PAGE

Alessio Micheli, Ante Graovac, Douglas J. Klein, 2009 .

A QSAR study of A1adenosine receptor antagonists by a new neural network model

Alessio Micheli, Alessandro Sperduti, Antonina Starita, 2001 .

Structured parallel implementation of Tree Echo State Network model selection

Claudio Gallicchio, Alessio Micheli, Marco Danelutto, 2015, PARCO.

The Recursive Neural Networks Approach to QSPR/QSAR: A Methodology Proposal

Alessio Micheli, Antonina Starita, A. Micheli, 2004 .

Computational Intelligence and Bioengineering

Alessio Micheli, Alessandro Sperduti, Francesco Masulli, 2009 .

Robust Malware Classification via Deep Graph Networks on Call Graph Topologies

Alessio Micheli, Fabio Martinelli, Francesco Mercaldo, 2021, ESANN.

Spectral Bounds for Graph Echo State Network Stability

A. Micheli, C. Gallicchio, Domenico Tortorella, 2022, 2022 International Joint Conference on Neural Networks (IJCNN).

Federated Reservoir Computing Neural Networks

Daniele Di Sarli, A. Micheli, D. Bacciu, 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

Edge-based sequential graph generation with recurrent neural networks

A. Micheli, D. Bacciu, Marco Podda, 2020, Neurocomputing.

Computational Intelligence and Bioengineering - Essays in Memory of Antonina Starita

Alessio Micheli, Alessandro Sperduti, Francesco Masulli, 2009, Computational Intelligence and Bioengineering.

The Infinite Contextual Graph Markov Model

A. Micheli, D. Bacciu, Federico Errica, 2022, ICML.

A Fair Comparison of Graph Neural Networks for Graph Classification

A. Micheli, D. Bacciu, Federico Errica, 2019, ICLR.

Adaptive Contextual Processing of Structured Data by Recursive Neural Networks: A Survey of Computational Properties

Alessio Micheli, Alessandro Sperduti, Barbara Hammer, 2007, Perspectives of Neural-Symbolic Integration.

An experimental characterization of reservoir computing in ambient assisted living applications

A. Micheli, D. Bacciu, C. Gallicchio, 2014, Neural Computing and Applications.

Leave Graphs Alone: Addressing Over-Squashing without Rewiring

A. Micheli, Domenico Tortorella, 2022, ArXiv.

A Deep Generative Model for Fragment-Based Molecule Generation

A. Micheli, D. Bacciu, Marco Podda, 2020, AISTATS.

Randomized Machine Learning Approaches: Recent Developments and Challenges

Claudio Gallicchio, Alessio Micheli, José David Martín-Guerrero, 2017, ESANN.

Echo State Property of Deep Reservoir Computing Networks

A. Micheli, C. Gallicchio, 2017, Cognitive Computation.

Towards learning trustworthily, automatically, and with guarantees on graphs: An overview

M. Bianchini, F. Scarselli, A. Micheli, 2022, Neurocomputing.

Modeling Edge Features with Deep Bayesian Graph Networks

Davide Bacciu, Alessio Micheli, Federico Errica, 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

Italian Machine Learning and Data Mining research: The last years

Fabrizio Riguzzi, Donato Malerba, Davide Bacciu, 2013, Intelligenza Artificiale.

Deep reservoir computing: A critical experimental analysis

Claudio Gallicchio, Alessio Micheli, Luca Pedrelli, 2017, Neurocomputing.

Time Series Clustering with Deep Reservoir Computing

Claudio Gallicchio, Gonzalo Joya, Miguel A. Atencia Ruiz, 2020, ICANN.

An ambient intelligence approach for learning in smart robotic environments

Davide Bacciu, Claudio Gallicchio, Alessio Micheli, 2019, Comput. Intell..

A cognitive robotic ecology approach to self-configuring and evolving AAL systems

Davide Bacciu, Claudio Gallicchio, Alessio Micheli, 2015, Eng. Appl. Artif. Intell..

Predicting User Movements in Heterogeneous Indoor Environments by Reservoir Computing

Stefano Chessa, Davide Bacciu, Paolo Barsocchi, 2011, IJCAI 2011.

Application of Cascade Correlation Networks for Structures to Chemistry

Alessio Micheli, Alessandro Sperduti, Antonina Starita, 2004, Applied Intelligence.

Prediction of the Glass Transition Temperature of (Meth)Acrylic Polymers Containing Phenyl Groups by Recursive Neural Network

Alessio Micheli, Antonina Starita, Maria Rosaria Tine, 2007 .

Feature-wise Competitive Repetition Suppression Learning for Gene Data Clustering and Feature Ranking

Davide Bacciu, Alessio Micheli, Antonina Starita, 2007 .

RANKING BY COMPETITIVE REPETITION SUPPRESSION LEARNING WITH APPLICATION TO GENE DATA ANALYSIS

Davide Bacciu, Alessio Micheli, Antonina Starita, 2007 .

Detecting Socialization Events in Ageing People: The Experience of the DOREMI Project

Stefano Chessa, Davide Bacciu, Claudio Gallicchio, 2016, 2016 12th International Conference on Intelligent Environments (IE).

Recursive self-organizing network models

Alessio Micheli, Alessandro Sperduti, Barbara Hammer, 2004, Neural Networks.

A general framework for unsupervised processing of structured data

Alessio Micheli, Alessandro Sperduti, Barbara Hammer, 2004, Neurocomputing.

EchoBay: Design and Optimization of Echo State Networks under Memory and Time Constraints

Claudio Gallicchio, Alessio Micheli, Marco D. Santambrogio, 2020, ACM Trans. Archit. Code Optim..

EchoBay

A. Micheli, M. Santambrogio, C. Gallicchio, 2020, ACM Transactions on Architecture and Code Optimization.

Efficient Embedded Machine Learning applications using Echo State Networks

M. D. Santambrogio, L. Cerina, G. Franco, 2020, 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE).

A Gentle Introduction to Deep Learning for Graphs

Davide Bacciu, Alessio Micheli, Federico Errica, 2019, Neural Networks.

Graph Echo State Networks

Claudio Gallicchio, Alessio Micheli, A. Micheli, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Neural Network for Graphs: A Contextual Constructive Approach

Alessio Micheli, A. Micheli, 2009, IEEE Transactions on Neural Networks.

Comparison between DeepESNs and gated RNNs on multivariate time-series prediction pdf

Claudio Gallicchio, Alessio Micheli, Luca Pedrelli, 2018, ESANN.

Human activity recognition using multisensor data fusion based on Reservoir Computing

Claudio Gallicchio, Alessio Micheli, Filippo Palumbo, 2016, J. Ambient Intell. Smart Environ..

Design of deep echo state networks

Claudio Gallicchio, Alessio Micheli, Luca Pedrelli, 2018, Neural Networks.

Prediction of the Italian electricity price for smart grid applications

Claudio Gallicchio, Alessio Micheli, Marco Raugi, 2015, Neurocomputing.

A preliminary empirical comparison of recursive neural networks and tree kernel methods on regression tasks for tree structured domains

A. Micheli, A. Sperduti, F. Portera, 2005, Neurocomputing.

Tree Echo State Networks

Claudio Gallicchio, Alessio Micheli, A. Micheli, 2013, Neurocomputing.

An introduction to recursive neural networks and kernel methods for cheminformatics.

Alessio Micheli, Alessandro Sperduti, Antonina Starita, 2007, Current pharmaceutical design.

A New Neural Network Model for Contextual Processing of Graphs

Alessio Micheli, Antonio S. Sestito, A. Micheli, 2005, WIRN/NAIS.

Dealing with Graphs by Neural Networks

Alessio Micheli, Antonio S. Sestito, A. Micheli, 2005 .

Fisher kernel for tree structured data

A. Starita, L. Nicotra, A. Micheli, 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

A preliminary experimental comparison of recursive neural networks and a tree kernel method for QSAR/QSPR regression tasks

Alessio Micheli, Alessandro Sperduti, Filippo Portera, 2004, ESANN.

QSAR/QSPR Studies by Kernel Machines, Recursive Neural Networks and Their Integration

Alessio Micheli, Alessandro Sperduti, Filippo Portera, 2003, WIRN.

Recursive Processing of Structured Domains in Machine Learning -PhD Thesis, TD-13/03

Alessio Micheli, A. Micheli, 2003 .

Predicting Thermodynamic Properties from Molecular Structures by Recursive Neural Networks

Alessio Micheli, Alessandro Sperduti, Antonina Starita, 2004 .

Design of new biologically active molecules by recursive neural networks

Alessio Micheli, Alessandro Sperduti, Antonina Starita, 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

Analysis of the Internal Representations Developed by Neural Networks for Structures Applied to Quantitative Structure-Activity Relationship Studies of Benzodiazepines

Alessio Micheli, Alessandro Sperduti, Antonina Starita, 2001, J. Chem. Inf. Comput. Sci..

Local Lyapunov exponents of deep echo state networks

Claudio Gallicchio, Alessio Micheli, Luca Silvestri, 2018, Neurocomputing.

Why Layering in Recurrent Neural Networks? A DeepESN Survey

Claudio Gallicchio, Alessio Micheli, A. Micheli, 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

Recursive Neural Networks for Cheminformatics: QSPR for Polymeric Compounds (Towards Biomaterials Design)

Alessio Micheli, Celia Duce, Roberto Solaro, 2009, Computational Intelligence and Bioengineering.

Addressing Heterophily in Node Classification with Graph Echo State Networks

A. Micheli, Domenico Tortorella, 2023, arXiv.org.

Beyond Homophily with Graph Echo State Networks

A. Micheli, Domenico Tortorella, 2022, ESANN.

Fast and Deep Graph Neural Networks

Claudio Gallicchio, Alessio Micheli, A. Micheli, 2019, AAAI.

Architectural and Markovian factors of echo state networks

Claudio Gallicchio, Alessio Micheli, A. Micheli, 2011, Neural Networks.

Universal Approximation Capability of Cascade Correlation for Structures

Alessio Micheli, Alessandro Sperduti, Barbara Hammer, 2005, Neural Computation.

Exploiting vertices states in GraphESN by weighted nearest neighbor

Claudio Gallicchio, Alessio Micheli, A. Micheli, 2011, ESANN.

TreeESN: a Preliminary Experimental Analysis

Claudio Gallicchio, Alessio Micheli, A. Micheli, 2010, ESANN.

Internet of Robotic Things-Converging Sensing / Actuating , Hypoconnectivity , Artificial Intelligence and IoT Platforms

Stefano Chessa, Alessandro Saffiotti, Davide Bacciu, 2017 .

On the need of machine learning as a service for the internet of things

Stefano Chessa, Davide Bacciu, Claudio Gallicchio, 2017, IML.

Phase Transition Adaptation

Claudio Gallicchio, Alessio Micheli, Luca Silvestri, 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

Ring Reservoir Neural Networks for Graphs

Claudio Gallicchio, Alessio Micheli, A. Micheli, 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

Reservoir Topology in Deep Echo State Networks

Claudio Gallicchio, Alessio Micheli, A. Micheli, 2019, ICANN.

Randomized Recurrent Neural Networks

Peter Tiño, Claudio Gallicchio, Alessio Micheli, 2018, ESANN.

Localizing Tortoise Nests by Neural Networks

Stefano Chessa, Alessio Micheli, Roberto Barbuti, 2016, PloS one.

Machine learning approaches for identifying prey handling activity in otariid pinnipeds pdf

Jane Hunter, Alessio Micheli, Stefano Chessa, 2020, ArXiv.

A Comparative Analysis of SVM and IDNN for Identifying Penguin Activities

Stefano Chessa, Jane Hunter, Alessio Micheli, 2017, Appl. Artif. Intell..

Dynamic Graph Echo State Networks pdf

Alessio Micheli, Domenico Tortorella, A. Micheli, 2021, ESANN.

Graph Mixture Density Networks

A. Micheli, D. Bacciu, Federico Errica, 2020, ICML.

Is Rewiring Actually Helpful in Graph Neural Networks?

A. Micheli, Domenico Tortorella, 2023, ArXiv.

A preliminary evaluation of Echo State Networks for Brugada syndrome classification

Giovanna Maria Dimitri, A. Micheli, C. Gallicchio, 2021, 2021 IEEE Symposium Series on Computational Intelligence (SSCI).

TEACHING - Trustworthy autonomous cyber-physical applications through human-centred intelligence

Davide Bacciu, Claudio Gallicchio, Alessio Micheli, 2021, 2021 IEEE International Conference on Omni-Layer Intelligent Systems (COINS).

A Deep Graph Network–Enhanced Sampling Approach to Efficiently Explore the Space of Reduced Representations of Proteins

A. Micheli, D. Bacciu, Federico Errica, 2021, Frontiers in Molecular Biosciences.

Deep Reservoir Computing

Claudio Gallicchio, Alessio Micheli, A. Micheli, 2021, Reservoir Computing.

Benchmarking Reservoir and Recurrent Neural Networks for Human State and Activity Recognition

Davide Bacciu, Claudio Gallicchio, Alessio Micheli, 2021, IWANN.

Classification of Biochemical Pathway Robustness with Neural Networks for Graphs

A. Micheli, P. Milazzo, Marco Podda, 2020, BIOSTEC.

Probabilistic Learning on Graphs via Contextual Architectures

Alessio Micheli, Federico Errica, Davide Bacciu, 2020, J. Mach. Learn. Res..

Evaluation of hierarchical structured representations for QSPR studies of small molecules and polymers by recursive neural networks.

Alessio Micheli, Antonina Starita, Celia Duce, 2009, Journal of molecular graphics & modelling.

Accelerating the identification of informative reduced representations of proteins with deep learning for graphs pdf

Davide Bacciu, Alessio Micheli, Federico Errica, 2020, ArXiv.

A learning system for automatic Berg Balance Scale score estimation

Davide Bacciu, Claudio Gallicchio, Alessio Micheli, 2017, Eng. Appl. Artif. Intell..

Pyramidal Reservoir Graph Neural Network

A. Micheli, C. Gallicchio, F. Bianchi, 2021, Neurocomputing.

Deep Learning for Graphs

Davide Bacciu, Alessio Micheli, A. Micheli, 2019, INNSBDDL.

Embeddings and Representation Learning for Structured Data pdf

Claudio Gallicchio, Alessio Micheli, Alessandro Sperduti, 2019, ESANN.

A General Framework for Self-Organizing Structure Processing Neural Networks

Alessio Micheli, Alessandro Sperduti, Barbara Hammer, 2003 .

A general framework for unsupervisedprocessing of structuredd ata

Alessio Micheli, Alessandro Sperduti, Barbara Hammer, 2004 .

Generative Kernels for Tree-Structured Data

Davide Bacciu, Alessio Micheli, Alessandro Sperduti, 2018, IEEE Transactions on Neural Networks and Learning Systems.

Fast Spectral Radius Initialization for Recurrent Neural Networks

Claudio Gallicchio, Alessio Micheli, Luca Pedrelli, 2019, INNSBDDL.

Guest Editorial Special Issue on New Frontiers in Extremely Efficient Reservoir Computing

A. Micheli, A. Hirose, C. Gallicchio, 2020, IEEE Trans. Neural Networks Learn. Syst..

Architectural richness in deep reservoir computing

A. Micheli, C. Gallicchio, 2022, Neural Computing and Applications.

Echo State Property of Deep Reservoir Computing Networks

Claudio Gallicchio, Alessio Micheli, A. Micheli, 2017, Cognitive Computation.

Predicting Physical-Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks

Alessio Micheli, Alessandro Sperduti, Antonina Starita, 2006, J. Chem. Inf. Model..

Deep Echo State Networks for Diagnosis of Parkinson's Disease

A. Micheli, C. Gallicchio, L. Pedrelli, 2018, ESANN.

Deep Echo State Network (DeepESN): A Brief Survey pdf

Claudio Gallicchio, Alessio Micheli, A. Micheli, 2017, ArXiv.

Experimental Analysis of Deep Echo State Networks for Ambient Assisted Living

Claudio Gallicchio, Alessio Micheli, A. Micheli, 2017, AI*AAL@AI*IA.

Hierarchical Temporal Representation in Linear Reservoir Computing

Claudio Gallicchio, Alessio Micheli, Luca Pedrelli, 2017, Neural Advances in Processing Nonlinear Dynamic Signals.

Deep Tree Echo State Networks

Claudio Gallicchio, Alessio Micheli, A. Micheli, 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

Addressing Heterophily in Node Classification with Graph Echo State Networks

A. Micheli, Domenico Tortorella, 2023, Neurocomputing.

Gated Echo State Networks: a preliminary study

Claudio Gallicchio, Alessio Micheli, Daniele Di Sarli, 2020, 2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA).

Question Classification with Untrained Recurrent Embeddings

Claudio Gallicchio, Alessio Micheli, Daniele Di Sarli, 2019, AI*IA.

Continuous Blood Pressure Estimation Through Optimized Echo State Networks

Claudio Gallicchio, Alessio Micheli, Marco D. Santambrogio, 2019, ICANN.

Deep Reservoir Neural Networks for Trees

Claudio Gallicchio, Alessio Micheli, A. Micheli, 2019, Inf. Sci..

Combining Memory and Non-linearity in Echo State Networks

Claudio Gallicchio, Alessio Micheli, Eleonora Di Gregorio, 2018, ICANN.

Robotic Ubiquitous Cognitive Ecology for Smart Homes

Stefano Chessa, Alessandro Saffiotti, Davide Bacciu, 2015, Journal of Intelligent & Robotic Systems.

A Reservoir Computing Approach for Balance Assessment

Claudio Gallicchio, Alessio Micheli, Federico Vozzi, 2015, AALTD@PKDD/ECML.

User Movements Forecasting by Reservoir Computing Using Signal Streams Produced by Mote-Class Sensors

Stefano Chessa, Paolo Barsocchi, Claudio Gallicchio, 2011, MOBILIGHT.

Text classification by untrained sentence embeddings

Daniele Di Sarli, A. Micheli, C. Gallicchio, 2020, Intelligenza Artificiale.

Distributed Neural Computation over WSN in Ambient Intelligence

Stefano Chessa, Davide Bacciu, Claudio Gallicchio, 2013, ISAmI.

An Experimental Evaluation of Reservoir Computation for Ambient Assisted Living

Stefano Chessa, Davide Bacciu, Paolo Barsocchi, 2012, WIRN.

A Markovian characterization of redundancy in echo state networks by PCA

Claudio Gallicchio, Alessio Micheli, A. Micheli, 2010, ESANN.

RSS-based Robot Localization in Critical Environments using Reservoir Computing

Claudio Gallicchio, Alessio Micheli, Mauro Dragone, 2016, ESANN.

Preliminary Experimental Analysis of Reservoir Computing Approach for Balance Assessment

Claudio Gallicchio, Alessio Micheli, Federico Vozzi, 2015, AALTD@PKDD/ECML.

Learning context-aware mobile robot navigation in home environments

Alessandro Saffiotti, Davide Bacciu, Claudio Gallicchio, 2014, IISA 2014, The 5th International Conference on Information, Intelligence, Systems and Applications.

Forecast-Driven Enhancement of Received Signal Strength (RSS)-Based Localization Systems

Stefano Chessa, Paolo Barsocchi, Claudio Gallicchio, 2013, ISPRS Int. J. Geo Inf..

Deep Reservoir Computing: A Critical Analysis

Claudio Gallicchio, Alessio Micheli, A. Micheli, 2016, ESANN.

Robot Localization by Echo State Networks Using RSS

Stefano Chessa, Claudio Gallicchio, Alessio Micheli, 2013, WIRN.

A Generative Multiset Kernel for Structured Data

Davide Bacciu, Alessio Micheli, Alessandro Sperduti, 2012, ICANN.

Prediction of Polymer Properties from their Structure by Recursive Neural Networks

Alessio Micheli, Antonina Starita, Maria Rosaria Tine, 2006 .

Formal Determination of Context in Contextual Recursive Cascade Correlation Networks

Alessio Micheli, Alessandro Sperduti, Diego Sona, 2003, ICANN.