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2020 – today
- 2024
[i14]Marianna Nezhurina, Lucia Cipolina-Kun, Mehdi Cherti, Jenia Jitsev:
Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models. CoRR abs/2406.02061 (2024)- [i13]Jan Lewen, Max Pargmann, Mehdi Cherti, Jenia Jitsev, Robert Pitz-Paal, Daniel Maldonado Quinto:
Inverse Deep Learning Ray Tracing for Heliostat Surface Prediction. CoRR abs/2408.10802 (2024) - [i12]Karsten Roth, Vishaal Udandarao, Sebastian Dziadzio, Ameya Prabhu, Mehdi Cherti, Oriol Vinyals, Olivier Hénaff, Samuel Albanie, Matthias Bethge, Zeynep Akata:
A Practitioner's Guide to Continual Multimodal Pretraining. CoRR abs/2408.14471 (2024) - [i11]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:
Application-Driven Exascale: The JUPITER Benchmark Suite. CoRR abs/2408.17211 (2024) - 2023
- [c9]Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, Jenia Jitsev:
Reproducible Scaling Laws for Contrastive Language-Image Learning. CVPR 2023: 2818-2829 - [c8]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:
DataComp: In search of the next generation of multimodal datasets. NeurIPS 2023 - [i10]Mehdi Cherti, Alexander Czernik, Stefan Kesselheim, Frederic Effenberger, Jenia Jitsev:
A Comparative Study on Generative Models for High Resolution Solar Observation Imaging. CoRR abs/2304.07169 (2023) - [i9]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 Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt:
DataComp: In search of the next generation of multimodal datasets. CoRR abs/2304.14108 (2023) - 2022
- [c7]Mehdi Cherti, Jenia Jitsev:
Effect of pre-training scale on intra- and inter-domain, full and few-shot transfer learning for natural and X-Ray chest images. IJCNN 2022: 1-9 - [c6]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:
LAION-5B: An open large-scale dataset for training next generation image-text models. NeurIPS 2022 - [i8]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:
LAION-5B: An open large-scale dataset for training next generation image-text models. CoRR abs/2210.08402 (2022) - [i7]Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, Jenia Jitsev:
Reproducible scaling laws for contrastive language-image learning. CoRR abs/2212.07143 (2022) - 2021
- [c5]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:
JUWELS Booster - A Supercomputer for Large-Scale AI Research. ISC Workshops 2021: 453-468 - [i6]Mehdi Cherti, Jenia Jitsev:
Effect of large-scale pre-training on full and few-shot transfer learning for natural and medical images. CoRR abs/2106.00116 (2021) - [i5]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:
JUWELS Booster - A Supercomputer for Large-Scale AI Research. CoRR abs/2108.11976 (2021)
2010 – 2019
- 2019
- [i4]Léonard Boussioux, Tomás Giro-Larraz, Charles Guille-Escuret, Mehdi Cherti, Balázs Kégl:
InsectUp: Crowdsourcing Insect Observations to Assess Demographic Shifts and Improve Classification. CoRR abs/1906.11898 (2019) - 2018
- [b1]Mehdi Cherti:
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). University of Paris-Saclay, France, 2018 - [i3]Balázs Kégl, Mehdi Cherti, Akin Kazakçi:
Spurious samples in deep generative models: bug or feature? CoRR abs/1810.01876 (2018) - 2017
- [c4]Mehdi Cherti, Balázs Kégl, Akin Kazakçi:
De novo drug design with deep generative models : an empirical study. ICLR (Workshop) 2017 - [c3]Mehdi Cherti, Balázs Kégl, Akin Kazakçi:
Out-of-class novelty generation: an experimental foundation. ICLR (Workshop) 2017 - [c2]Mehdi Cherti, Balázs Kégl, Akin Kazakçi:
Out-of-Class Novelty Generation : An Experimental Foundation. ICTAI 2017: 1312-1319 - [i2]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:
Machine learning for classification and quantification of monoclonal antibody preparations for cancer therapy. CoRR abs/1705.07099 (2017) - 2016
- [c1]Akin Kazakçi, Mehdi Cherti, Balázs Kégl:
Digits that are not: Generating new types through deep neural nets. ICCC 2016: 188-196 - [i1]Akin Kazakçi, Mehdi Cherti, Balázs Kégl:
Digits that are not: Generating new types through deep neural nets. CoRR abs/1606.04345 (2016)
Coauthor Index
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