Jitsev, Jenia

LAION-5B: An open large-scale dataset for training next generation image-text models

Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al. (2022) · arXiv

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 1,022

Reproducible Scaling Laws for Contrastive Language-Image Learning

Cherti, M., Beaumont, R., Wightman, R., Wortsman, M., Ilharco, G., Gordon, C., Schuhmann, C., Schmidt, L., & Jitsev, J. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 673

Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

Merrill, M. A., Shaw, A. G., Carlini, N., Li, B., Raj, H., Bercovich, I., Shi, L., Shin, J. Y., Walshe, T., Buchanan, E. K., et al. (2026) · arXiv

AI-based Problem Solving and PlanningArtificial Intelligence Applications

Cited by 392

LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

Schuhmann, C., Vencu, R., Beaumont, R., Kaczmarczyk, R., Mullis, C., Katta, A., Coombes, T., Jitsev, J., & Komatsuzaki, A. (2021) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 358

LAION-5B: An Open Large-Scale Dataset for Training Next Generation Image-Text Models

Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al. (2022) · Advances in Neural Information Processing Systems 35

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 325

DataComp: In search of the next generation of multimodal datasets

Gadre, S. Y., Ilharco, G., Fang, A., Hayase, J., Smyrnis, G., Nguyen, T., Marten, R., Wortsman, M., Ghosh, D., Zhang, J., et al. (2023) · Advances in Neural Information Processing Systems 36

Multimodal Machine Learning ApplicationsImage Retrieval and Classification Techniques

Cited by 120

Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models

Nezhurina, M., Cipolina-Kun, L., Cherti, M., & Jitsev, J. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 21

Language models scale reliably with over-training and on downstream tasks

Gadre, S. Y., Smyrnis, G., Shankar, V., Gururangan, S., Wortsman, M., Shao, R., Mercat, J., Fang, A., Li, J., Keh, S., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 5

OpenThoughts: Data Recipes for Reasoning Models

Guha, E., Marten, R., Keh, S., Raoof, N., Smyrnis, G., Bansal, H., Nezhurina, M., Mercat, J., Vu, T., Sprague, Z., et al. (2025) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 1