Mehdi Cherti

According to our database, Mehdi Cherti authored at least 18 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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, Ana Gonzalez-Nicolas, Eric B. Gregory, Kaveh Haghighi Mood, Thorsten Hater, Jenia Jitsev, Chelsea Maria John, Jan H. Meinke, Catrin I. Meyer, Pavel Mezentsev, Jan-Oliver Mirus, Stepan Nassyr, Carolin Penke, Manoel Römmer, Ujjwal Sinha, Benedikt von St. Vieth, Olaf Stein, Estela Suarez, Dennis Willsch, Ilya Zhukov

CoRR, 2024

A Practitioner's Guide to Continual Multimodal Pretraining.

Karsten Roth, Vishaal Udandarao, Sebastian Dziadzio, Ameya Prabhu, Mehdi Cherti, Oriol Vinyals, Olivier Hénaff, Samuel Albanie, Matthias Bethge, Zeynep Akata

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

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

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

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, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah M. Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander J. Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

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, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, Jenia Jitsev

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

Proceedings of the International Joint Conference on Neural Networks, 2022

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

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, Alexander Schug, Alexandre Strube, Roshni Kamath, Martin G. Schultz, Morris Riedel, Thomas Lippert

Proceedings of the High Performance Computing - ISC High Performance Digital 2021 International Workshops, Frankfurt am Main, Germany, June 24, 2021

2019

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

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

PhD thesis, 2018

Spurious samples in deep generative models: bug or feature?

Balázs Kégl, Mehdi Cherti, Akin Kazakçi

CoRR, 2018

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

Out-of-class novelty generation: an experimental foundation.

Mehdi Cherti, Balázs Kégl, Akin Kazakçi

Proceedings of the 5th International Conference on Learning Representations, 2017

De novo drug design with deep generative models : an empirical study.

Mehdi Cherti, Balázs Kégl, Akin Kazakçi

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Digits that are not: Generating new types through deep neural nets.

Akin Kazakçi, Mehdi Cherti, Balázs Kégl

Proceedings of the Seventh International Conference on Computational Creativity, 2016