AI-based mobile application to fight antibiotic resistance

Autor: Marco Pascucci, Guilhem Royer, Jakub Adamek, Mai Al Asmar, David Aristizabal, Laetitia Blanche, Amine Bezzarga, Guillaume Boniface-Chang, Alex Brunner, Christian Curel, Gabriel Dulac-Arnold, Rasheed M. Fakhri, Nada Malou, Clara Nordon, Vincent Runge, Franck Samson, Ellen Sebastian, Dena Soukieh, Jean-Philippe Vert, Christophe Ambroise, Mohammed-Amin Madoui

Publikováno v: Nature Communications, Vol 12, Iss 1, Pp 1-10 (2021)

Antimicrobial resistance is a major global health threat and its development is promoted by antibiotic misuse. Here, the authors present an offline smartphone application for automated and standardized antibiotic susceptibility testing, to be deploye

Externí odkaz: https://doaj.org/article/b52321a049cb442db94c01d55d0fb7d5

Challenges of real-world reinforcement learning: definitions, benchmarks and analysis

Autor: Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Nir Levine, Jerry Li, Todd Hester, Sven Gowal

Publikováno v: Machine Learning. 110:2419-2468

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is beginning to show some successes in real-world scenarios. However, much of the research advances in RL are hard to leverage in real-world systems due to a seri

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::48400b369d20140e3d3fb921d47b7dd2
https://doi.org/10.1007/s10994-021-05961-4

Deep Q-learning from Demonstrations

Autor: Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, Gabriel Dulac-Arnold, John Agapiou, Joel Leibo, Audrunas Gruslys

Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of data before they reach reasonable performance. In fact, their performance

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d2603999dd3fc6b0bb5f7dadefd67bf1
http://arxiv.org/abs/1704.03732

Fast Reinforcement Learning with Large Action Sets Using Error-Correcting Output Codes for MDP Factorization

Autor: Philippe Preux, Ludovic Denoyer, Gabriel Dulac-Arnold, Patrick Gallinari

Publikováno v: Machine Learning and Knowledge Discovery in Databases
European Conference on Machine Learning
European Conference on Machine Learning, Sep 2012, Bristol, United Kingdom. pp.180-194, ⟨10.1007/978-3-642-33486-3_12⟩
Machine Learning and Knowledge Discovery in Databases ISBN: 9783642334856
ECML/PKDD (2)

The use of Reinforcement Learning in real-world scenarios is strongly limited by issues of scale. Most RL learning algorithms are unable to deal with problems composed of hundreds or sometimes even dozens of possible actions, and therefore cannot be

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2a4cc63368051242e48cf23e036e2570
https://hal.inria.fr/hal-00747729/file/version.officielle.Springer.pdf