A Visual Object Recognition System Invariant to Scale and Rotation. - Yasuomi D. Sato; Jenia Jitsev; Christoph von der Malsburg (ICANN (1), 2008)

Experience-driven formation of parts-based representations in a model of layered visual memory. - Jenia Jitsev; Christoph von der Malsburg (Frontiers Comput. Neurosci., 2009)

Visual Object Detection by Specifying the Scale and Rotation Transformations. - Yasuomi D. Sato; Jenia Jitsev; Christoph von der Malsburg (ICONIP (2), 2010)

Off-line memory reprocessing following on-line unsupervised learning strongly improves recognition performance in a hierarchical visual memory. - Jenia Jitsev; Christoph von der Malsburg (IJCNN, 2010)

Dynamic link models for global decision making with binding-by-synchrony. - Yasuomi D. Sato; Jenia Jitsev; Thomas Burwick; Christoph von der Malsburg (NaBIC, 2010)

A Gabor Wavelet Pyramid-Based Object Detection Algorithm. - Yasuomi D. Sato; Jenia Jitsev; Jörg Bornschein; Daniela Pamplona; Christian Keck; Christoph von der Malsburg (ISNN (2), 2011)

Information-Theoretic Connectivity-Based Cortex Parcellation. - Nico S. Gorbach; Silvan Siep; Jenia Jitsev; Corina Melzer; Marc Tittgemeyer (MLINI, 2011)

Hierarchical Information-Based Clustering for Connectivity-Based Cortex Parcellation. - Nico S. Gorbach; Christoph Schütte; Corina Melzer; Mathias Goldau; Olivia Sujazow; Jenia Jitsev; Tania S. Douglas; Marc Tittgemeyer (Frontiers Neuroinformatics, 2011)

Learning from Delayed Reward und Punishment in a Spiking Neural Network Model of Basal Ganglia with Opposing D1/D2 Plasticity. - Jenia Jitsev; Nobi Abraham; Abigail Morrison; Marc Tittgemeyer (ICANN (1), 2012)

Learning from positive and negative rewards in a spiking neural network model of basal ganglia. - Jenia Jitsev; Abigail Morrison; Marc Tittgemeyer (IJCNN, 2012)

Self-generated Off-line Memory Reprocessing Strongly Improves Generalization in a Hierarchical Recurrent Neural Network. - Jenia Jitsev (ICANN, 2014)

Towards Prediction of Turbulent Flows at High Reynolds Numbers Using High Performance Computing Data and Deep Learning. - Mathis Bode; Michael Gauding; Jens Henrik Göbbert; Baohao Liao; Jenia Jitsev; Heinz Pitsch (ISC Workshops, 2018)

Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows. - Mathis Bode; Michael Gauding; Zeyu Lian; Dominik Denker; Marco Davidovic; Konstantin Kleinheinz; Jenia Jitsev; Heinz Pitsch (CoRR, 2019)

Remote Sensing Big Data Classification with High Performance Distributed Deep Learning. - Rocco Sedona; Gabriele Cavallaro; Jenia Jitsev; Alexandre Strube; Morris Riedel; Jón Atli Benediktsson (Remote. Sens., 2019)

Obstacle Tower Without Human Demonstrations: How Far a Deep Feed-Forward Network Goes with Reinforcement Learning. - Marco Pleines; Jenia Jitsev; Mike Preuss; Frank Zimmer (CoG, 2020)

Scaling Up a Multispectral Resnet-50 to 128 GPUs. - Rocco Sedona; Gabriele Cavallaro; Jenia Jitsev; Alexandre Strube; Morris Riedel; Matthias Book (IGARSS, 2020)

Super-Resolution of Large Volumes of Sentinel-2 Images with High Performance Distributed Deep Learning. - Run Zhang; Gabriele Cavallaro; Jenia Jitsev (IGARSS, 2020)

Prediction of Acoustic Fields Using a Lattice-Boltzmann Method and Deep Learning. - Mario Rüttgers; Seong-Ryong Koh; Jenia Jitsev; Wolfgang Schröder 001; Andreas Lintermann (ISC Workshops, 2020)

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)

Generalization over different cellular automata rules learned by a deep feed-forward neural network. - Marcel Aach; Jens Henrik Göbbert; Jenia Jitsev (CoRR, 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)

LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs. - Christoph Schuhmann; Richard Vencu; Romain Beaumont; Robert Kaczmarczyk; Clayton Mullis; Aarush Katta; Theo Coombes; Jenia Jitsev; Aran Komatsuzaki (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)

Adversarial domain adaptation to reduce sample bias of a high energy physics event classifier *. - Jose M. Clavijo; Paul Glaysher; Jenia Jitsev; Judith M. Katzy (Mach. Learn. Sci. Technol., 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)

OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models. - Anas Awadalla; Irena Gao; Josh Gardner 001; Jack Hessel; Yusuf Hanafy; Wanrong Zhu; Kalyani Marathe; Yonatan Bitton; Samir Yitzhak Gadre; Shiori Sagawa; Jenia Jitsev; Simon Kornblith; et al. (CoRR, 2023)

DataComp-LM: In search of the next generation of training sets for language models. - Jeffrey Li; Alex Fang; Georgios Smyrnis; Maor Ivgi; Matt Jordan; Samir Yitzhak Gadre; Hritik Bansal; Etash Kumar Guha; Sedrick Scott Keh; Kushal Arora; Saurabh Garg; Rui Xin; et al. (NeurIPS, 2024)

Resolving Discrepancies in Compute-Optimal Scaling of Language Models. - Tomer Porian; Mitchell Wortsman; Jenia Jitsev; Ludwig Schmidt; Yair Carmon (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)

Language models scale reliably with over-training and on downstream tasks. - Samir Yitzhak Gadre; Georgios Smyrnis; Vaishaal Shankar; Suchin Gururangan; Mitchell Wortsman; Rulin Shao; Jean Mercat; Alex Fang; Jeffrey Li; Sedrick Keh; Rui Xin; Marianna Nezhurina; et al. (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)

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)

Language models scale reliably with over-training and on downstream tasks. - Samir Yitzhak Gadre; Georgios Smyrnis; Vaishaal Shankar; Suchin Gururangan; Mitchell Wortsman; Rulin Shao; Jean Mercat; Alex Fang; Jeffrey Li; Sedrick Keh; Rui Xin; Marianna Nezhurina; et al. (ICLR, 2025)

Project Alexandria: Towards Freeing Scientific Knowledge from Copyright Burdens via LLMs. - Christoph Schuhmann; Gollam Rabby; Ameya Prabhu; Tawsif Ahmed; Andreas Hochlehnert; Huu Nguyen; Nick Akinci Heidrich; Ludwig Schmidt; Robert Kaczmarczyk; Sören Auer; Jenia Jitsev; Matthias Bethge (CoRR, 2025)

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)

Learning in Compact Spaces with Approximately Normalized Transformers. - Jörg K. H. Franke; Urs Spiegelhalter; Marianna Nezhurina; Jenia Jitsev; Frank Hutter; Michael Hefenbrock (CoRR, 2025)

OpenThoughts: Data Recipes for Reasoning Models. - Etash Kumar Guha; Ryan Marten; Sedrick Keh; Negin Raoof; Georgios Smyrnis; Hritik Bansal; Marianna Nezhurina; Jean Mercat; Trung Vu 002; Zayne Sprague; Ashima Suvarna; Benjamin Feuer; et al. (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)