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Tami Myriam
Tami Myriam
enseignante-chercheuse en statistical learning, Université Paris Saclay, CentraleSupélec
Verified email at centralesupelec.fr - Homepage
Title
Cited by
Cited by
Year
Semi-supervised semantic segmentation with cross-consistency training
Y Ouali, C Hudelot, M Tami
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020
8862020
An overview of deep semi-supervised learning
Y Ouali, C Hudelot, M Tami
arXiv preprint arXiv:2006.05278, 2020
4572020
Autoregressive unsupervised image segmentation
Y Ouali, C Hudelot, M Tami
Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020
1022020
Spatial contrastive learning for few-shot classification
Y Ouali, C Hudelot, M Tami
Machine Learning and Knowledge Discovery in Databases. Research Track …, 2021
602021
An overview of deep semi-supervised learning. arXiv 2020
Y Ouali, C Hudelot, M Tami
arXiv preprint arXiv:2006.05278, 2006
392006
Bridging few-shot learning and adaptation: New challenges of support-query shift
E Bennequin, V Bouvier, M Tami, A Toubhans, C Hudelot
Machine Learning and Knowledge Discovery in Databases. Research Track …, 2021
182021
Robust domain adaptation: Representations, weights and inductive bias
V Bouvier, P Very, C Chastagnol, M Tami, C Hudelot
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2021
172021
A scale-invariant sorting criterion to find a causal order in additive noise models
A Reisach, M Tami, C Seiler, A Chambaz, S Weichwald
Advances in Neural Information Processing Systems 36, 2024
162024
Open-set likelihood maximization for few-shot learning
M Boudiaf, E Bennequin, M Tami, A Toubhans, P Piantanida, C Hudelot, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
152023
An overview of deep semi-supervised learning (2020)
Y Ouali, C Hudelot, M Tami
arXiv preprint arXiv:2006.05278, 2006
132006
Simple sorting criteria help find the causal order in additive noise models
AG Reisach, M Tami, C Seiler, A Chambaz, S Weichwald
stat 1050, 31, 2023
72023
Few-shot image classification benchmarks are too far from reality: Build back better with semantic task sampling
E Bennequin, M Tami, A Toubhans, C Hudelot
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
72022
Transductive learning for textual few-shot classification in API-based embedding models
P Colombo, V Pellegrain, M Boudiaf, V Storchan, M Tami, IB Ayed, ...
arXiv preprint arXiv:2310.13998, 2023
62023
Target consistency for domain adaptation: when robustness meets transferability
Y Ouali, V Bouvier, M Tami, C Hudelot
arXiv preprint arXiv:2006.14263, 2020
52020
Smooth and consistent probabilistic regression trees
S Alkhoury, E Devijver, M Clausel, M Tami, E Gaussier
Advances in Neural Information Processing Systems 33, 11345-11355, 2020
52020
EM algorithm estimation of a structural equation model for the longitudinal study of the quality of life
A Barbieri, M Tami, X Bry, D Azria, S Gourgou, C Bascoul‐Mollevi, ...
Statistics in medicine 37 (6), 1031-1046, 2018
52018
Estimation of structural equation models with factors by EM algorithm
M Tami, X Bry, C Lavergne
HAL 2014, 2014
52014
Uncertain trees: Dealing with uncertain inputs in regression trees
M Tami, M Clausel, E Devijver, A Dulac, E Gaussier, S Janaqi, M Chebre
arXiv preprint arXiv:1810.11698, 2018
42018
Model-Agnostic Few-Shot Open-Set Recognition
M Boudiaf, E Bennequin, M Tami, C Hudelot, A Toubhans, P Piantanida, ...
arXiv preprint arXiv:2206.09236, 2022
32022
Domain-invariant representations: A look on compression and weights
V Bouvier, C Hudelot, C Chastagnol, P Very, M Tami
32019
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Articles 1–20