Digits that are not: Generating new types through deep neural nets. - Akin Kazakçi; Mehdi Cherti; Balázs Kégl (ICCC, 2016)

De novo drug design with deep generative models : an empirical study. - Mehdi Cherti; Balázs Kégl; Akin Kazakçi (ICLR (Workshop), 2017)

Out-of-class novelty generation: an experimental foundation. - Mehdi Cherti; Balázs Kégl; Akin Kazakçi (ICLR (Workshop), 2017)

Machine learning for classification and quantification of monoclonal antibody preparations for cancer therapy. - Laetitia Le; Camille Marini; Alexandre Gramfort; David Nguyen; Mehdi Cherti; Sana Tfaili; Ali Tfayli; Arlette Baillet-Guffroy; Patrice Prognon; Pierre Chaminade; Eric Caudron; Balázs Kégl (CoRR, 2017)

Spurious samples in deep generative models: bug or feature? - Balázs Kégl; Mehdi Cherti; Akin Kazakçi (CoRR, 2018)

Deep generative neural networks for novelty generation : a foundational framework, metrics and experiments. (Réseaux profonds génératifs pour la génération de nouveauté : fondations, métriques et expériences). - Mehdi Cherti (2018)

InsectUp: Crowdsourcing Insect Observations to Assess Demographic Shifts and Improve Classification. - Léonard Boussioux; Tomás Giro-Larraz; Charles Guille-Escuret; Mehdi Cherti; Balázs Kégl (CoRR, 2019)

JUWELS Booster - A Supercomputer for Large-Scale AI Research. - Stefan Kesselheim; Andreas Herten; Kai Krajsek; Jan Ebert; Jenia Jitsev; Mehdi Cherti; Michael Langguth; Bing Gong; Scarlet Stadtler; Amirpasha Mozaffari; Gabriele Cavallaro; Rocco Sedona; et al. (ISC Workshops, 2021)

Effect of large-scale pre-training on full and few-shot transfer learning for natural and medical images. - Mehdi Cherti; Jenia Jitsev (CoRR, 2021)

Effect of pre-training scale on intra- and inter-domain, full and few-shot transfer learning for natural and X-Ray chest images. - Mehdi Cherti; Jenia Jitsev (IJCNN, 2022)

LAION-5B: An open large-scale dataset for training next generation image-text models. - Christoph Schuhmann; Romain Beaumont; Richard Vencu; Cade Gordon; Ross Wightman; Mehdi Cherti; Theo Coombes; Aarush Katta; Clayton Mullis; Mitchell Wortsman; Patrick Schramowski; Srivatsa Kundurthy; et al. (NeurIPS, 2022)

Reproducible Scaling Laws for Contrastive Language-Image Learning. - Mehdi Cherti; Romain Beaumont; Ross Wightman; Mitchell Wortsman; Gabriel Ilharco; Cade Gordon; Christoph Schuhmann; Ludwig Schmidt; Jenia Jitsev (CVPR, 2023)

DataComp: In search of the next generation of multimodal datasets. - Samir Yitzhak Gadre; Gabriel Ilharco; Alex Fang; Jonathan Hayase; Georgios Smyrnis; Thao Nguyen; Ryan Marten; Mitchell Wortsman; Dhruba Ghosh; Jieyu Zhang 001; Eyal Orgad; Rahim Entezari; et al. (NeurIPS, 2023)

A Comparative Study on Generative Models for High Resolution Solar Observation Imaging. - Mehdi Cherti; Alexander Czernik; Stefan Kesselheim; Frederic Effenberger; Jenia Jitsev (CoRR, 2023)

A Practitioner's Guide to Real-World Continual Multimodal Pretraining. - Vishaal Udandarao; Karsten Roth; Sebastian Dziadzio; Ameya Prabhu; Mehdi Cherti; Oriol Vinyals; Olivier J. Hénaff; Samuel Albanie; Zeynep Akata; Matthias Bethge (NeurIPS, 2024)

Application-Driven Exascale: The JUPITER Benchmark Suite. - Andreas Herten; Sebastian Achilles; Damian Alvarez; Jayesh Badwaik; Eric Behle; Mathis Bode; Thomas Breuer; Daniel Caviedes-Voullième; Mehdi Cherti; Adel Dabah; Salem El Sayed; Wolfgang Frings; et al. (SC, 2024)

Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models. - Marianna Nezhurina; Lucia Cipolina-Kun; Mehdi Cherti; Jenia Jitsev (CoRR, 2024)

Inverse Deep Learning Ray Tracing for Heliostat Surface Prediction. - Jan Lewen; Max Pargmann; Mehdi Cherti; Jenia Jitsev; Robert Pitz-Paal; Daniel Maldonado Quinto (CoRR, 2024)

A Practitioner's Guide to Continual Multimodal Pretraining. - Karsten Roth; Vishaal Udandarao; Sebastian Dziadzio; Ameya Prabhu; Mehdi Cherti; Oriol Vinyals; Olivier J. Hénaff; Samuel Albanie; Matthias Bethge; Zeynep Akata (CoRR, 2024)

Scalable heliostat surface predictions from focal spots: Sim-to-Real transfer of inverse Deep Learning Raytracing. - Jan Lewen; Max Pargmann; Jenia Jitsev; Mehdi Cherti; Robert Pitz-Paal; Daniel Maldonado Quinto (CoRR, 2025)

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets. - Marianna Nezhurina; Tomer Porian; Giovanni Pucceti; Tommie Kerssies; Romain Beaumont; Mehdi Cherti; Jenia Jitsev (CoRR, 2025)

A Good CREPE needs more than just Sugar: Investigating Biases in Compositional Vision-Language Benchmarks. - Vishaal Udandarao; Mehdi Cherti; Shyamgopal Karthik; Jenia Jitsev; Samuel Albanie; Matthias Bethge (CoRR, 2025)