Roberto Esposito - researchr alias

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Viewing Publication 1 - 58 from 58

2024

A preferential interpretation of MultiLayer Perceptrons in a conditional logic with typicalityMario Alviano, Francesco Bartoli, Marco Botta, Roberto Esposito, Laura Giordano 0001, Daniele Theseider Dupré. ijar, 164:109065, January 2024. [doi]

Experimenting With Normalization Layers in Federated Learning on Non-IID ScenariosBruno Casella, Roberto Esposito, Antonio Sciarappa, Carlo Cavazzoni, Marco Aldinucci. access, 12:47961-47971, 2024. [doi]

2023

Pooling critical datasets with Federated LearningYasir Arfat, Gianluca Mittone, Iacopo Colonnelli, Fabrizio D'Ascenzo, Roberto Esposito, Marco Aldinucci. pdp 2023: 329-337 [doi]

Invariant Representations with Stochastically Quantized Neural NetworksMattia Cerrato, Marius Köppel, Roberto Esposito, Stefan Kramer 0001. AAAI 2023: 6962-6970 [doi]

Boosting Methods for Federated LearningRoberto Esposito, Mirko Polato, Marco Aldinucci. sebd 2023: 439-448 [doi]

Experimenting with Emerging RISC-V Systems for Decentralised Machine LearningGianluca Mittone, Nicolò Tonci, Robert Birke, Iacopo Colonnelli, Doriana Medic, Andrea Bartolini, Roberto Esposito, Emanuele Parisi, Francesco Beneventi, Mirko Polato, Massimo Torquati, Luca Benini, Marco Aldinucci. cf 2023: 73-83 [doi]

1st Workshop on Federated Learning TechnologiesMirko Polato, Roberto Esposito, Walter Riviera, Zenglin Xu, Irwin King. WWW 2023: 1150 [doi]

FairSwiRL: fair semi-supervised classification with representation learningShuyi Yang, Mattia Cerrato, Dino Ienco, Ruggero G. Pensa, Roberto Esposito. ml, 112(9):3051-3076, September 2023. [doi]

Fair Semi-supervised Representation Learning for Tabular Data ClassificationShuyi Yang, Mattia Cerrato, Dino Ienco, Ruggero G. Pensa, Roberto Esposito. sebd 2023: 488-496 [doi]

2022

Towards EXtreme scale technologies and accelerators for euROhpc hw/Sw supercomputing applications for exascale: The TEXTAROSSA approachGiovanni Agosta, Marco Aldinucci, Carlos Álvarez 0001, Roberto Ammendola, Yasir Arfat, Olivier Beaumont, Massimo Bernaschi, Andrea Biagioni, Tommaso Boccali, Bérenger Bramas, Carlo Brandolese, Barbara Cantalupo, Mauro Carrozzo, Daniele Cattaneo 0002, Alessandro Celestini, Massimo Celino, Iacopo Colonnelli, Paolo Cretaro, Pasqua D'Ambra, Marco Danelutto, Roberto Esposito, Lionel Eyraud-Dubois, Antonio Filgueras, William Fornaciari, Ottorino Frezza, Andrea Galimberti, Francesco Giacomini, Brice Goglin, Daniele Gregori, Abdou Guermouche, Francesco Iannone, Michal Kulczewski, Francesca Lo Cicero, Alessandro Lonardo, Alberto Riccardo Martinelli, Michele Martinelli, Xavier Martorell, Giuseppe Massari, Simone Montangero, Gianluca Mittone, Raymond Namyst, Ariel Oleksiak, Paolo Palazzari, Pier Stanislao Paolucci, Federico Reghenzani, Cristian Rossi, Sergio Saponara, Francesco Simula, Federico Terraneo, Samuel Thibault, Massimo Torquati, Matteo Turisini, Piero Vicini, Miquel Vidal, Davide Zoni, Giuseppe Zummo. mam, 95:104679, November 2022. [doi]

Towards a Conditional and Multi-preferential Approach to Explainability of Neural Network Models in Computational Logic (Extended Abstract)Mario Alviano, Francesco Bartoli, Marco Botta, Roberto Esposito, Laura Giordano 0001, Valentina Gliozzi, Daniele Theseider Dupré. aiia 2022: 64-72 [doi]

Model Checking Verification of MultiLayer Perceptrons in Datalog: a Many-valued Approach with TypicalityFrancesco Bartoli, Marco Botta, Roberto Esposito, Laura Giordano 0001, Daniele Theseider Dupré. datalog 2022: 54-67 [doi]

From Common Sense Reasonig to Neural Network Models: a Conditional and Multi-preferential Approach for Explainability and Neuro-Symbolic IntegrationFrancesco Bartoli, Marco Botta, Roberto Esposito, Laura Giordano 0001, Valentina Gliozzi, Daniele Theseider Dupré. ki 2022: 66-78 [doi]

Benchmarking FedAvg and FedCurv for Image Classification TasksBruno Casella, Roberto Esposito, Carlo Cavazzoni, Marco Aldinucci. itadata 2023: 99-110 [doi]

Boosting the Federation: Cross-Silo Federated Learning without Gradient DescentMirko Polato, Roberto Esposito, Marco Aldinucci. ijcnn 2022: 1-10 [doi]

Dealing With Multipositive Unlabeled Learning Combining Metric Learning and Deep ClusteringAmedeo Racanati, Roberto Esposito, Dino Ienco. access, 10:51839-51849, 2022. [doi]

2021

TEXTAROSSA: Towards EXtreme scale Technologies and Accelerators for euROhpc hw/Sw Supercomputing Applications for exascaleGiovanni Agosta, Daniele Cattaneo 0002, William Fornaciari, Andrea Galimberti, Giuseppe Massari, Federico Reghenzani, Federico Terraneo, Davide Zoni, Carlo Brandolese, Massimo Celino, Francesco Iannone, Paolo Palazzari, Giuseppe Zummo, Massimo Bernaschi, Pasqua D'Ambra, Sergio Saponara, Marco Danelutto, Massimo Torquati, Marco Aldinucci, Yasir Arfat, Barbara Cantalupo, Iacopo Colonnelli, Roberto Esposito, Alberto Riccardo Martinelli, Gianluca Mittone, Olivier Beaumont, Bérenger Bramas, Lionel Eyraud-Dubois, Brice Goglin, Abdou Guermouche, Raymond Namyst, Samuel Thibault, Antonio Filgueras, Miquel Vidal, Carlos Álvarez 0001, Xavier Martorell, Ariel Oleksiak, Michal Kulczewski, Alessandro Lonardo, Piero Vicini, Francesca Lo Cicero, Francesco Simula, Andrea Biagioni, Paolo Cretaro, Ottorino Frezza, Pier Stanislao Paolucci, Matteo Turisini, Francesco Giacomini, Tommaso Boccali, Simone Montangero, Roberto Ammendola. dsd 2021: 286-294 [doi]

NeuNAC: A novel fragile watermarking algorithm for integrity protection of neural networksMarco Botta, Davide Cavagnino, Roberto Esposito. isci, 576:228-241, 2021. [doi]

HPC Application Cloudification: The StreamFlow Toolkit (Invited Paper)Iacopo Colonnelli, Barbara Cantalupo, Roberto Esposito, Matteo Pennisi, Concetto Spampinato, Marco Aldinucci. hipeac 2021: [doi]

Fairness and Neural NetworksRoberto Esposito. sebd 2021: [doi]

Ranking Algorithms for Word Ordering in Surface RealizationAlessandro Mazzei, Mattia Cerrato, Roberto Esposito, Valerio Basile. information, 12(8):337, 2021. [doi]

☆: A generic framework for semi-supervised inductive learningShuyi Yang, Dino Ienco, Roberto Esposito, Ruggero G. Pensa. ijon, 447:102-117, 2021. [doi]

An Inductive Framework for Semi-supervised Learning (Discussion Paper)Shuyi Yang, Dino Ienco, Roberto Esposito, Ruggero G. Pensa. sebd 2021: 371-378 [doi]

2020

2019

Taxonomic and Whole Object Constraints: A Deep ArchitectureMattia Cerrato, Edoardo Arnaudo, Valentina Gliozzi, Roberto Esposito. cogsci 2019: 1465-1471 [doi]

Partitioned Least SquaresRoberto Esposito, Mattia Cerrato, Marco Locatelli 0001. aiia 2019: 180-192 [doi]

2017

A Neural Network Model for Taxonomic Responding with Realistic Visual InputsGiorgia Fenoglio, Roberto Esposito, Valentina Gliozzi. cogsci 2017: [doi]

2015

Autonomous abnormal behaviour detection in intelligence surveillance and reconnaissance applicationsRosa Meo, Roberto Esposito, Marco Botta, S. Viola, C. M. Choor, V. Mellano, F. Ciaramaglia. rtsi 2015: 334-340 [doi]

Prediction and interpretation of the lipophilicity of small peptidesAlessia Visconti, Giuseppe Ermondi, Giulia Caron, Roberto Esposito. jcamd, 29(4):361-370, 2015. [doi]

2013

CDoT: Optimizing MAP Queries on TreesRoberto Esposito, Daniele P. Radicioni, Alessia Visconti. aiia 2013: 481-492 [doi]

2011

Restructuring the Gene Ontology to emphasise regulative pathways and to improve gene similarity queriesAlessia Visconti, Roberto Esposito, Francesca Cordero. ijcbdd, 4(3):220-238, 2011. [doi]

Tackling the DREAM Challenge for Gene Regulatory Networks Reverse EngineeringAlessia Visconti, Roberto Esposito, Francesca Cordero. aiia 2011: 372-382 [doi]

2010

BREVE: An HMPerceptron-Based Chord Recognition SystemDaniele P. Radicioni, Roberto Esposito. In Zbigniew W. Ras, Alicja Wieczorkowska, editors, Advances in Music Information Retrieval. Volume 274 of Studies in Computational Intelligence, pages 143-164, Springer, 2010. [doi]

2009

CarpeDiem: Optimizing the Viterbi Algorithm and Applications to Supervised Sequential LearningRoberto Esposito, Daniele P. Radicioni. jmlr, 10:1851-1880, 2009. [doi]

OpenCDLig: a free web application for sharing resources about cyclodextrin/ligand complexesRoberto Esposito, Giuseppe Ermondi, Giulia Caron. jcamd, 23(9):669-675, 2009. [doi]

Empirical Assessment of Two Strategies for Optimizing the Viterbi AlgorithmRoberto Esposito, Daniele P. Radicioni. aiia 2009: 141-150 [doi]

2007

2006

Answering constraint-based mining queries on itemsets using previous materialized resultsRoberto Esposito, Rosa Meo, Marco Botta. jiis, 26(1):95-111, 2006. [doi]

A Conditional Model for Tonal AnalysisDaniele P. Radicioni, Roberto Esposito. ismis 2006: 652-661 [doi]

2005

Experimental comparison between bagging and Monte Carlo ensemble classificationRoberto Esposito, Lorenza Saitta. icml 2005: 209-216 [doi]

Optimization of Association Rules Extraction Through Exploitation of Context Dependent ConstraintsArianna Gallo, Roberto Esposito, Rosa Meo, Marco Botta. aiia 2005: 258-269 [doi]

2004

2003

Analyzing ensemble learning in the framework of Monte Carlo theoryRoberto Esposito. PhD thesis, University of Turin, Italy, 2003. [doi]

Explaining Bagging with Monte Carlo TheoryRoberto Esposito, Lorenza Saitta. aiia 2003: 189-200 [doi]

Monte Carlo Theory as an Explanation of Bagging and BoostingRoberto Esposito, Lorenza Saitta. IJCAI 2003: 499-504

2002

Is a Greedy Covering Strategy an Extreme Boosting?Roberto Esposito, Lorenza Saitta. ismis 2002: 94-102 [doi]

2001

Boosting as a Monte Carlo AlgorithmRoberto Esposito, Lorenza Saitta. aiia 2001: 11-19 [doi]

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