| Laion-5b: An open large-scale dataset for training next generation image-text models C Schuhmann, R Beaumont, R Vencu, C Gordon, R Wightman, M Cherti, ... Advances in Neural Information Processing Systems 35, 25278-25294, 2022 | 526 | 2022 |
| Reproducible scaling laws for contrastive language-image learning M Cherti, R Beaumont, R Wightman, M Wortsman, G Ilharco, C Gordon, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 69 | 2023 |
| Effect of pre-training scale on intra-and inter-domain, full and few-shot transfer learning for natural and X-ray chest images M Cherti, J Jitsev 2022 International Joint Conference on Neural Networks (IJCNN), 1-9, 2022 | 20* | 2022 |
| DataComp: In search of the next generation of multimodal datasets SY Gadre, G Ilharco, A Fang, J Hayase, G Smyrnis, T Nguyen, R Marten, ... arXiv preprint arXiv:2304.14108, 2023 | 16 | 2023 |
| Optimization of classification and regression analysis of four monoclonal antibodies from Raman spectra using collaborative machine learning approach LMM Le, B Kégl, A Gramfort, C Marini, D Nguyen, M Cherti, S Tfaili, ... Talanta 184, 260-265, 2018 | 15 | 2018 |
| Out-of-class novelty generation: an experimental foundation M Cherti, B Kégl, A Kazakçı 2017 IEEE 29th International Conference on Tools with Artificial …, 2017 | 15 | 2017 |
| Digits that are not: Generating new types through deep neural nets A Kazakçı, C Mehdi, B Kégl arXiv preprint arXiv:1606.04345, 2016 | 11 | 2016 |
| LAION-5B: An open large-scale dataset for training next generation image-text models.(10 2022) C Schuhmann, R Beaumont, R Vencu, C Gordon, R Wightman, M Cherti, ... URL https://arxiv. org/abs/2210.08402, 2022 | 10 | 2022 |
| Juwels booster–a supercomputer for large-scale ai research S Kesselheim, A Herten, K Krajsek, J Ebert, J Jitsev, M Cherti, M Langguth, ... High Performance Computing: ISC High Performance Digital 2021 International …, 2021 | 10 | 2021 |
| The RAMP framework: from reproducibility to transparency in the design and optimization of scientific workflows B Kégl, A Boucaud, M Cherti, A Kazakci, A Gramfort, G Lemaitre, ... | 8 | 2018 |
| De novo drug design with deep generative models: an empirical study M Cherti, B Kégl, A Kazakçı ICLR workshop, 2017 | 8 | 2017 |
| Spurious samples in deep generative models: Bug or feature? B Kégl, M Cherti, A Kazakçı arXiv preprint arXiv:1810.01876, 2018 | 5 | 2018 |
| Deep generative neural networks for novelty generation : a foundational framework, metrics and experiments M Cherti Université Paris-Saclay, 2018 | 3 | 2018 |
| Machine learning for classification and quantification of monoclonal antibody preparations for cancer therapy L Le, C Marini, A Gramfort, D Nguyen, M Cherti, S Tfaili, A Tfayli, ... arXiv preprint arXiv:1705.07099, 2017 | 3 | 2017 |
| Effect of pre-training scale on intra-and inter-domain transfer for natural and X-Ray chest images M Cherti, J Jitsev MedNeurIPS 2021 workshop, 2021 | 1 | 2021 |
| InsectUp: Crowdsourcing Insect Observations to Assess Demographic Shifts and Improve Classification L Boussioux, T Giro-Larraz, C Guille-Escuret, M Cherti, B Kégl arXiv preprint arXiv:1906.11898, 2019 | 1 | 2019 |
| A Comparative Study on Generative Models for High Resolution Solar Observation Imaging M Cherti, A Czernik, S Kesselheim, F Effenberger, J Jitsev arXiv preprint arXiv:2304.07169, 2023 | | 2023 |
| DataComp: In search of the next generation of multimodal datasets S Yitzhak Gadre, G Ilharco, A Fang, J Hayase, G Smyrnis, T Nguyen, ... arXiv e-prints, arXiv: 2304.14108, 2023 | | 2023 |
| Generative Deep Learning with High-Resolution SDO EUV Images of the Sun F Effenberger, M Cherti, S Kesselheim, R Vasile, J Jitsev AGU Fall Meeting Abstracts 2022, NG42A-05, 2022 | | 2022 |
| Generative Adversarial Deep Learning with Solar Images F Effenberger, R Vasile, M Cherti, S Kesselheim, J Jitsev AGU Fall Meeting Abstracts 2021, NG41A-04, 2021 | | 2021 |
Balazs KEGLResearch scientist, Paris AI leader, Noah's Ark Lab, Huawei ResearchVerified email at huawei.com
