--- title: "Who cites Revisiting Unreasonable — 134 citing papers" description: "134 papers of the Elamur Research library cite «Revisiting Unreasonable Effectiveness of Data in Deep Learning Era», found in their…" canonical: "https://research.elamur.ai/free/en/papers/sun-2017-revisiting-unreasonable-effectiveness-data-deep/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/sun-2017-revisiting-unreasonable-effectiveness-data-deep/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Revisiting Unreasonable Effectiveness of Data in…](https://md.research.elamur.ai/free/en/papers/sun-2017-revisiting-unreasonable-effectiveness-data-deep/) / cited by # Who cites «Revisiting Unreasonable Effectiveness of Data in Deep Learning Era» 134 papers of our library cite it (of the 149,439 whose bibliographies we read) · 21 with a page here, the most cited first · [← back to «Revisiting Unreasonable Effectiveness of Data in Deep Learning Era»](https://md.research.elamur.ai/free/en/papers/sun-2017-revisiting-unreasonable-effectiveness-data-deep/) 1. [**An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale arXiv:2010.11929**Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, et al.*2020 · 23,726 citations*](https://md.research.elamur.ai/free/en/papers/dosovitskiy-2020-image-worth-16x16-words-transformers/) 2. **[Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://md.research.elamur.ai/free/en/papers/jia-2021-scaling-up-visual-vision-language/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Chao Jia, Yinfei Yang, Ye Xia, et al.*2021 · 1,615 citations* 3. [**Zero-Shot Text-to-Image Generation arXiv:2102.12092**Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, et al.*2021 · 1,528 citations*](https://md.research.elamur.ai/free/en/papers/ramesh-2021-zero-shot-text-image-generation/) 4. [**LAION-5B: An open large-scale dataset for training next generation image-text models arXiv:2210.08402**Christoph Schuhmann, Romain Beaumont, Richard Vencu, et al.*2022 · 1,485 citations*](https://md.research.elamur.ai/free/en/papers/schuhmann-2022-laion-5b-open-large-scale/) 5. **[VideoBERT: A Joint Model for Video and Language Representation Learning](https://md.research.elamur.ai/free/en/papers/sun-2019-videobert-joint-model-video-language/) [IEEE/CVF International Conference on Computer Vision (ICCV)](https://md.research.elamur.ai/free/en/venues/ieee-cvf-international-conference-on-computer-vision-iccv/)**Chen Sun, Austin Myers, Carl Vondrick, et al.*2019 · 1,345 citations* 6. **[Reproducible scaling laws for contrastive language-image learning](https://md.research.elamur.ai/free/en/papers/cherti-2022-reproducible-scaling-laws-contrastive-language/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Mehdi Cherti, Romain Beaumont, Ross Wightman, et al.*2022 · 769 citations* 7. [**Training data-efficient image transformers & distillation through attention arXiv:2012.12877**Hugo Touvron, Matthieu Cord, Matthijs Douze, et al.*2020 · 524 citations*](https://md.research.elamur.ai/free/en/papers/touvron-2020-training-data-efficient-image-transformers/) 8. [**Deep Learning Scaling is Predictable, Empirically arXiv:1712.00409**Joel Hestness, Sharan Narang, Newsha Ardalani, et al.*2017 · 518 citations*](https://md.research.elamur.ai/free/en/papers/hestness-2017-deep-learning-scaling-predictable-empirically/) 9. [**Green AI arXiv:1907.10597**Roy Schwartz, Jesse Dodge, Noah A. Smith, et al.*2019 · 389 citations*](https://md.research.elamur.ai/free/en/papers/schwartz-2019-green-ai/) 10. **[Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout](https://md.research.elamur.ai/free/en/papers/tan-2019-learning-navigate-unseen-environments-back/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Hao Tan, Licheng Yu, Mohit Bansal*2019 · 358 citations* 11. **[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning](https://md.research.elamur.ai/free/en/papers/srinivasan-2021-wit-wikipedia-based-image-text/) [International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)](https://md.research.elamur.ai/free/en/venues/international-acm-sigir-conference-on-research-and-development-in-information/)**Krishna Srinivasan, Karthik Raman, Jiecao Chen, et al.*2021 · 315 citations* 12. [**On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective arXiv:2302.12095**Jindong Wang, Xixu Hu, Wenxin Hou, et al.*2023 · 176 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-robustness-chatgpt-adversarial-out-distribution/) 13. [**Scaling Vision with Sparse Mixture of Experts arXiv:2106.05974**Carlos Riquelme, Joan Puigcerver, Basil Mustafa, et al.*2021 · 128 citations*](https://md.research.elamur.ai/free/en/papers/riquelme-2021-scaling-vision-sparse-mixture-experts/) 14. [**A Constructive Prediction of the Generalization Error Across Scales arXiv:1909.12673**Jonathan S. Rosenfeld, Amir Rosenfeld, Yonatan Belinkov, et al.*2019 · 120 citations*](https://md.research.elamur.ai/free/en/papers/rosenfeld-2019-constructive-prediction-generalization-error-across/) 15. **[What's in the Box? An Analysis of Undesirable Content in the Common Crawl Corpus](https://md.research.elamur.ai/free/en/papers/luccioni-2021-what-s-box-analysis-undesirable/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alexandra Luccioni, Joseph Viviano*2021 · 111 citations* 16. [**Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints arXiv:2212.05055**Aran Komatsuzaki, Joan Puigcerver, James Lee-Thorp, et al.*2022 · 98 citations*](https://md.research.elamur.ai/free/en/papers/komatsuzaki-2022-sparse-upcycling-training-mixture-experts/) 17. **[POTATO: The Portable Text Annotation Tool](https://md.research.elamur.ai/free/en/papers/pei-2022-potato-portable-text-annotation-tool/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiaxin Pei, Aparna Ananthasubramaniam, Xingyao Wang, et al.*2022 · 90 citations* 18. [**Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future**Jan-Christoph Klie, Bonnie Webber, Iryna Gurevych*2023 · 73 citations*](https://md.research.elamur.ai/free/en/papers/klie-2023-annotation-error-detection-analyzing-past/) 19. **[Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning](https://md.research.elamur.ai/free/en/papers/nacar-2024-enhancing-semantic-similarity-understanding-arabic/) [Studies in computational intelligence](https://md.research.elamur.ai/free/en/venues/studies-in-computational-intelligence/)**Omer Nacar, Anis Koubaa*2024 · 16 citations* 20. **[NLP-CIMAT at SemEval-2026 Task 9: LLM-Based One-Shot and Cross-Lingual Data Augmentation for Polarization Detection](https://md.research.elamur.ai/free/en/papers/reyes-2026-nlp-cimat-semeval-2026-task/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Miriam Calderon-Reyes, Fernando Sanchez-Vega, Adrian Pastor Lopez Monroy*2026* 21. **[Survey on Vision-language Pre-training (视觉语言预训练综述)](https://md.research.elamur.ai/free/en/papers/yin-2022-vision-language-pre/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Jiong Yin (殷炯), Zhedong Zhang (张哲东), Yuhan Gao (高宇涵), et al.*2022* 113 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/sun-2017-revisiting-unreasonable-effectiveness-data-deep/) — Revisiting Unreasonable Effectiveness of Data in Deep Learning Era