| 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 | 874 | 2020 |
| An overview of deep semi-supervised learning Y Ouali, C Hudelot, M Tami arXiv preprint arXiv:2006.05278, 2020 | 451 | 2020 |
| Autoregressive unsupervised image segmentation Y Ouali, C Hudelot, M Tami Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 102 | 2020 |
| Spatial contrastive learning for few-shot classification Y Ouali, C Hudelot, M Tami Machine Learning and Knowledge Discovery in Databases. Research Track …, 2021 | 60 | 2021 |
| An overview of deep semi-supervised learning. arXiv 2020 Y Ouali, C Hudelot, M Tami arXiv preprint arXiv:2006.05278, 2006 | 38 | 2006 |
| 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 | 18 | 2021 |
| 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 | 17 | 2021 |
| 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 | 15 | 2024 |
| 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 | 15 | 2023 |
| An overview of deep semi-supervised learning (2020) Y Ouali, C Hudelot, M Tami arXiv preprint arXiv:2006.05278, 2006 | 13 | 2006 |
| 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 | 7 | 2023 |
| 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 | 7 | 2022 |
| 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 | 6 | 2023 |
| Target consistency for domain adaptation: when robustness meets transferability Y Ouali, V Bouvier, M Tami, C Hudelot arXiv preprint arXiv:2006.14263, 2020 | 5 | 2020 |
| 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 | 5 | 2020 |
| 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 | 5 | 2018 |
| Estimation of structural equation models with factors by EM algorithm M Tami, X Bry, C Lavergne HAL 2014, 2014 | 5 | 2014 |
| 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 | 4 | 2018 |
| 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 | 3 | 2022 |
| Domain-invariant representations: A look on compression and weights V Bouvier, C Hudelot, C Chastagnol, P Very, M Tami | 3 | 2019 |
