Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Robert, Florian"'
Autor:
Robert, Florian, Calovoulos, Alexia, Facq, Laurent, Decoeur, Fanny, Gontier, Etienne, Grosset, Christophe F., de Senneville, Baudouin Denis
Accurately segmenting and individualizing cells in SEM images is a highly promising technique for elucidating tissue architecture in oncology. While current AI-based methods are effective, errors persist, necessitating time-consuming manual correctio
Externí odkaz:
http://arxiv.org/abs/2407.15817
Autor:
Badja, Cherif, Momen, Sophie, Koh, Gene Ching Chiek, Boushaki, Soraya, Roumeliotis, Theodoros I., Kozik, Zuza, Jones, Ian, Bousgouni, Vicky, Dias, João M.L., Krokidis, Marios G., Young, Jamie, Chen, Hongwei, Yang, Ming, Docquier, France, Memari, Yasin, Valcarcel-Zimenez, Lorea, Gupta, Komal, Kong, Li Ren, Fawcett, Heather, Robert, Florian, Zhao, Salome, Degasperi, Andrea, Kumar, Yogesh, Davies, Helen, Harris, Rebecca, Frezza, Christian, Chatgilialoglu, Chryssostomos, Sarkany, Robert, Lehmann, Alan, Bakal, Chris, Choudhary, Jyoti, Fassihi, Hiva, Nik-Zainal, Serena
Publikováno v:
In Cell Reports 25 June 2024 43(6)
Counterfactual instances are a powerful tool to obtain valuable insights into automated decision processes, describing the necessary minimal changes in the input space to alter the prediction towards a desired target. Most previous approaches require
Externí odkaz:
http://arxiv.org/abs/2106.02597
Autor:
Maina, Samuel C., Bryant, Reginald E., Goal, William O., Samoilescu, Robert-Florian, Varshney, Kush R., Weldemariam, Komminist
In this paper, we investigate the effect of machine learning based anonymization on anomalous subgroup preservation. In particular, we train a binary classifier to discover the most anomalous subgroup in a dataset by maximizing the bias between the g
Externí odkaz:
http://arxiv.org/abs/1911.03674
Autor:
Mihalea, Andrei1 (AUTHOR) andrei.mihalea@stud.acs.upb.ro, Samoilescu, Robert-Florian1 (AUTHOR), Florea, Adina Magda1 (AUTHOR) robert.samoilescu@stud.acs.upb.ro
Publikováno v:
Sensors (14248220). Oct2023, Vol. 23 Issue 20, p8473. 22p.
Publikováno v:
Sensors, Vol 23, Iss 20, p 8473 (2023)
Autonomous driving is a complex task that requires high-level hierarchical reasoning. Various solutions based on hand-crafted rules, multi-modal systems, or end-to-end learning have been proposed over time but are not quite ready to deliver the accur
Externí odkaz:
https://doaj.org/article/747003de4ca84571bc600beabba95ba2
Akademický článek
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Publikováno v:
MATEC Web of Conferences, Vol 343, p 08015 (2021)
In the first part of the paper, the authors present the characteristics of the robots for all types of terrain. In the second part, two categories of robots are proposed: a robot with hybrid locomotion system and a modular robot. For the last categor
Externí odkaz:
https://doaj.org/article/59db121f016d47e8bc6ae67e0b5c66bd
Autor:
Robert, Florian1 (AUTHOR), Desroches-Castan, Agnès1 (AUTHOR), Bailly, Sabine1 (AUTHOR), Dupuis-Girod, Sophie1,2,3 (AUTHOR), Feige, Jean-Jacques1 (AUTHOR) jean-jacques.feige@cea.fr
Publikováno v:
Orphanet Journal of Rare Diseases. 1/7/2020, Vol. 15 Issue 1, p1-10. 10p.
Autor:
William Ogallo, Reginald E. Bryant, Komminist Weldemariam, Robert-Florian Samoilescu, Skyler Speakman, Kush R. Varshney, Celia Cintas, Aisha Walcott-Bryant, Samuel C. Maina
Publikováno v:
ICASSP
We investigate the effect of variational autoencoder (VAE) based data anonymization and its ability to preserve anomalous subgroup properties. We present a Utility Guaranteed Deep Privacy (UGDP) system which casts existing anomalous pattern detection
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a463e70e1e01bc799af5d2674b9df509
https://doi.org/10.1109/icassp40776.2020.9054495
https://doi.org/10.1109/icassp40776.2020.9054495