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 | 2711 | 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 | 608 | 2023 |
Datacomp: In search of the next generation of multimodal datasets SY Gadre, G Ilharco, A Fang, J Hayase, G Smyrnis, T Nguyen, R Marten, ... Advances in Neural Information Processing Systems 36, 2024 | 305 | 2024 |
Laion-5b: An open large-scale dataset for training next generation image-text models, 2022 C Schuhmann, R Beaumont, R Vencu, C Gordon, R Wightman, M Cherti, ... URL https://arxiv. org/abs/2210.08402, 2022 | 53 | 2022 |
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 | 34* | 2022 |
Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models M Nezhurina, L Cipolina-Kun, M Cherti, J Jitsev arXiv preprint arXiv:2406.02061, 2024 | 29 | 2024 |
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 | 20 | 2018 |
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 | 18 | 2021 |
Out-of-class novelty generation: an experimental foundation M Cherti, B Kégl, A Kazakçı 2017 IEEE 29th International Conference on Tools with Artificial …, 2017 | 16 | 2017 |
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, ... | 13 | 2018 |
Digits that are not: Generating new types through deep neural nets A Kazakçı, C Mehdi, B Kégl arXiv preprint arXiv:1606.04345, 2016 | 13 | 2016 |
De novo drug design with deep generative models: an empirical study M Cherti, B Kégl, A Kazakçı ICLR workshop, 2017 | 9 | 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 | 4 | 2018 |
Application-driven exascale: The JUPITER benchmark suite A Herten, S Achilles, D Alvarez, J Badwaik, E Behle, M Bode, T Breuer, ... arXiv preprint arXiv:2408.17211, 2024 | 3 | 2024 |
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 |
A Practitioner's Guide to Continual Multimodal Pretraining K Roth, V Udandarao, S Dziadzio, A Prabhu, M Cherti, O Vinyals, ... arXiv preprint arXiv:2408.14471, 2024 | 2 | 2024 |
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 | 2 | 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 |
Inverse Deep Learning Ray Tracing for Heliostat Surface Prediction J Lewen, M Pargmann, M Cherti, J Jitsev, R Pitz-Paal, DM Quinto arXiv preprint arXiv:2408.10802, 2024 | | 2024 |