BACK TO INDEX

Publications of Santiago Silva

Articles in journal, book chapters

  1. Irene Balelli, Santiago Silva, and Marco Lorenzi. A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations. Journal of Machine Learning for Biomedical Imaging, April 2022. [bibtex-entry]


Conference articles

  1. Jean Ogier Du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago Silva, Maria Telenczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, and Mathieu Andreux. FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. In NeurIPS 2022 - Thirty-sixth Conference on Neural Information Processing Systems, Proceedings of NeurIPS, New Orleans, United States, November 2022. [bibtex-entry]


  2. Irene Balelli, Santiago Silva, and Marco Lorenzi. A Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations. In International Conference on Information Processing in Medical Imaging, Information processing in medical imaging: proceedings of the 27th International Conference, IPMI 2021, Bornholm, Denmark, June 2021. Keyword(s): Federated Learning, Hierarchical Generative Model, Heterogeneity. [bibtex-entry]


  3. Santiago Silva, Andre Altmann, Boris Gutman, and Marco Lorenzi. Fed-BioMed: A general open-source frontendframework for federated learning in healthcare. In MICCAI 2020 - 23rd International Conference on Medical Image Computing and Computer Assisted Intervention - 1st Workshop on Distributed and Collaborative Learning, DCL: MICCAI Workshop on Distributed and Collaborative Learning, Lima/ Virtuel, Peru, pages 201-210, October 2020. Springer. Keyword(s): federated learning, healthcare, medical imaging. [bibtex-entry]


  4. Santiago Silva, Boris A Gutman, Eduardo Romero, Paul M. Thompson, Andre Altmann, and Marco Lorenzi. Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data. In International Symposium on Biomedical Imaging, Venice, Italy, April 2018. [bibtex-entry]


Internal reports

  1. Santiago Silva, Boris Gutman, Eduardo Romero, Paul M. Thompson, Andre Altmann, and Marco Lorenzi. Federated learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data (Supplementary Material). Research Report, Inria & Université Cote d'Azur, CNRS, I3S, Sophia Antipolis, France, October 2018. [bibtex-entry]


Miscellaneous

  1. Francesco Cremonesi, Marc Vesin, Sergen Cansiz, Yannick Bouillard, Irene Balelli, Lucia Innocenti, Santiago Silva, Samy-Safwan Ayed, Riccardo Taiello, Laetita Kameni, Richard Vidal, Fanny Orlhac, Christophe Nioche, Nathan Lapel, Bastien Houis, Romain Modzelewski, Olivier Humbert, Melek Önen, and Marco Lorenzi. Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications. Note: Working paper or preprint, April 2023. Keyword(s): Machine learning, Biomedical Application, Healthcare, Federated Learning Framework. [bibtex-entry]



BACK TO INDEX

Disclaimer:

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All person copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Les documents contenus dans ces répertoires sont rendus disponibles par les auteurs qui y ont contribué en vue d'assurer la diffusion à temps de travaux savants et techniques sur une base non-commerciale. Les droits de copie et autres droits sont gardés par les auteurs et par les détenteurs du copyright, en dépit du fait qu'ils présentent ici leurs travaux sous forme électronique. Les personnes copiant ces informations doivent adhérer aux termes et contraintes couverts par le copyright de chaque auteur. Ces travaux ne peuvent pas être rendus disponibles ailleurs sans la permission explicite du détenteur du copyright.

Last modified: Fri Feb 23 12:30:05 2024
Author: epione-publi.

This document was translated from BibTEX by bibtex2html