Margaux Luck
Margaux Luck
Mila - Quebec Artificial Intelligence Institute
Verified email at mila.quebec
Title
Cited by
Cited by
Year
Distribution matching losses can hallucinate features in medical image translation
JP Cohen, M Luck, S Honari
International conference on medical image computing and computer-assisted …, 2018
892018
Deep learning for patient-specific kidney graft survival analysis
M Luck, T Sylvain, H Cardinal, A Lodi, Y Bengio
arXiv preprint arXiv:1705.10245, 2017
622017
Rule-mining for the early prediction of chronic kidney disease based on metabolomics and multi-source data
M Luck, G Bertho, M Bateson, A Karras, A Yartseva, E Thervet, C Damon, ...
PloS one 11 (11), e0166905, 2016
162016
Energetics of endurance exercise in young horses determined by nuclear magnetic resonance metabolomics
MMH Luck, L Le Moyec, E Barrey, MN Triba, N Bouchemal, P Savarin, ...
Frontiers in physiology 6, 198, 2015
152015
Predictive modeling of tacrolimus dose requirement based on high‐throughput genetic screening
C Damon, M Luck, L Toullec, I Etienne, M Buchler, B Hurault de Ligny, ...
American Journal of Transplantation 17 (4), 1008-1019, 2017
122017
Metabolic profiling of 1H NMR spectra in chronic kidney disease with local predictive modeling
MM Luck, A Yartseva, G Bertho, E Thervet, P Beaune, N Pallet, C Damon
2015 IEEE 14th International Conference on Machine Learning and Applications …, 2015
82015
A Survey of Mobile Computing for the Visually Impaired
M Weiss, M Luck, R Girgis, C Pal, JP Cohen
arXiv preprint arXiv:1811.10120, 2018
72018
Navigation agents for the visually impaired: A sidewalk simulator and experiments
M Weiss, S Chamorro, R Girgis, M Luck, SE Kahou, JP Cohen, ...
Conference on Robot Learning, 1314-1327, 2020
52020
How to cure cancer (in images) with unpaired image translation
JP Cohen, M Luck, S Honari
52018
L1 logistic regression as a feature selection step for training stable classification trees for the prediction of severity criteria in imported malaria
L Talenti, M Luck, A Yartseva, N Argy, S Houzé, C Damon
arXiv preprint arXiv:1511.06663, 2015
42015
Urinary metabolic profiling of asymptomatic acute intermittent porphyria using a rule-mining-based algorithm
M Luck, C Schmitt, N Talbi, L Gouya, C Caradeuc, H Puy, G Bertho, ...
Metabolomics 14 (1), 10, 2018
22018
Rule-Mining based classification: a benchmark study
M Luck, N Pallet, C Damon
arXiv preprint arXiv:1706.10199, 2017
12017
On self-supervised multi-modal representation learning: An application to Alzheimer's disease
A Fedorov, L Wu, T Sylvain, M Luck, TP DeRamus, D Bleklov, SM Plis, ...
arXiv preprint arXiv:2012.13619, 2020
2020
Taxonomy of multimodal self-supervised representation learning
A Fedorov, T Sylvain, M Luck, L Wu, TP DeRamus, A Kirilin, D Bleklov, ...
arXiv preprint arXiv:2012.13623, 2020
2020
Cross-Modal Information Maximization for Medical Imaging: CMIM
T Sylvain, F Dutil, T Berthier, L Di Jorio, M Luck, D Hjelm, Y Bengio
arXiv preprint arXiv:2010.10593, 2020
2020
Learning to rank for censored survival data
M Luck, T Sylvain, JP Cohen, H Cardinal, A Lodi, Y Bengio
arXiv preprint arXiv:1806.01984, 2018
2018
ETUDE DE LA VARIABILITÉ INTERINDIVIDUELLE DE LA RÉPONSE PHARMACOCINÉTIQUE AU TACROLIMUS PAR UN SCREENING GÉNÉTIQUE À HAUT DÉBIT: O34
C Damond, M Luck, G Choukroun, J Subra, C Legendre, M Buchler, ...
Transplant International 29, 2016
2016
STUDY OF INTER-INDIVIDUAL VARIABILITY IN RESPONSE PHARMACOKINETICS TO TACROLIMUS BY A HIGH THROUGHPUT GENETIC SCREENING
C Damond, M Luck, G Choukroun, JF Subra, C Legendre, M Buchler, ...
TRANSPLANT INTERNATIONAL 29, 9-9, 2016
2016
NMR metabolomics in young endurance horses
M Luck, N Bouchemal, MN Triba, C Robert, E Barrey, L Le Moyec
8. Journées Scientifiques du Réseau Français de Métabolomique et Fluxomique …, 2014
2014
Predictive analysis of metabolomics data in chronic kidney disease using a subgroup discovery algorithm
M Luck, G Bertho, E Thervet, P Beaune, F d’Ormesson, M Bateson, ...
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Articles 1–20