Mehdi Cherti

Postdoc at Jülich Supercomputing Centre (JSC), Germany

I received my PhD in Machine Learning from Université Paris Saclay in 2018 followed by a Postdoc at Mines ParisTech. Currently, I am a postdoctoral researcher at Jülich Supercomputing Center (JSC), within the Scalable Learning and Multi-Purpose AI (SLAMPAI) Lab. I am also part of the LAION team.

My research focuses on deep generative models and how to use them to learn representations that can help to design genuinely novel and useful objects, with various applications to computational creativity and scientific domains.

I am also generally interested in learning models that can learn efficiently (transfer learning, few-shot learning, meta-learning) and generalize out of their training distribution (OOD generalization), enabling broader and more robust applicability.

News

Nov 21, 2022 Our LAION-5B: An open large-scale dataset for training next generation image-text models received an Outstanding paper award On NeurIPS Datasets and Benchmarks track.
Oct 13, 2022 Our LAION-5B: An open large-scale dataset for training next generation image-text models (announcement page, arXiv, poster ) paper was accepted at NeurIPS 2022 Datasets and Benchmarks track.
Jul 1, 2022 Effect of large-scale pre-training on full and few-shot transfer learning for natural and medical images
Jun 9, 2021 New preprint Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest Images, where we conduct large-scale pre-training on large source datasets of either natural (ImageNet-21k/1k) or medical chest X-Ray images and compare full and few-shot transfer using different target datasets from both natural and medical imaging domains. Code is available here.

Selected publications

  1. LAION-5B: An open large-scale dataset for training next generation image-text models
    Schuhmann, Christoph, Beaumont, Romain, Vencu, Richard, Gordon, Cade W, Wightman, Ross, Cherti, Mehdi, Coombes, Theo, Katta, Aarush, Mullis, Clayton, Wortsman, Mitchell, Schramowski, Patrick, Kundurthy, Srivatsa R, Crowson, Katherine, Schmidt, Ludwig, Kaczmarczyk, Robert, and Jitsev, Jenia
    In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track 2022
  2. Effect of large-scale pre-training on full and few-shot transfer learning for natural and medical images
    Cherti, Mehdi, and Jitsev, Jenia
    arXiv preprint arXiv:2106.00116 2021
  3. Optimization of classification and regression analysis of four monoclonal antibodies from Raman spectra using collaborative machine learning approach
    Le, Laetitia Minh Maı̈, Kégl, Balázs, Gramfort, Alexandre, Marini, Camille, Nguyen, David, Cherti, Mehdi, Tfaili, Sana, Tfayli, Ali, Baillet-Guffroy, Arlette, Prognon, Patrice, and others,
    Talanta 2018
  4. The RAMP framework: from reproducibility to transparency in the design and optimization of scientific workflows
    Kégl, Balázs, Boucaud, Alexandre, Cherti, Mehdi, Kazakci, Akin, Gramfort, Alexandre, Lemaitre, Guillaume, Bossche, Joris, Benbouzid, Djalel, and Marini, Camille
    2018
  5. Spurious samples in deep generative models: bug or feature?
    Kégl, Balázs, Cherti, Mehdi, and Kazakçı, Akın
    arXiv preprint arXiv:1810.01876 2018
  6. Digits that are not: Generating new types through deep neural nets
    Kazakçı, Akın, Mehdi, Cherti, and Kégl, Balázs
    In Proceedings of the Seventh International Conference on Computational Creativity 2016
  7. Out-of-class novelty generation: an experimental foundation
    Cherti, Mehdi, Kégl, Balázs, and Kazakçı, Akin
    In ICLR 2017 workshop
  8. De novo drug design with deep generative models: an empirical study
    Cherti, Mehdi, Kégl, Balázs, and Kazakçı, Akın
    In ICLR 2017 workshop