--- title: "Who cites Benchmarking Neural Network — 143 citing papers" description: "143 papers of the Elamur Research library cite «Benchmarking Neural Network Robustness to Common Corruptions and Perturbations»…" canonical: "https://research.elamur.ai/free/en/papers/hendrycks-2019-benchmarking-neural-network-robustness-common/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/hendrycks-2019-benchmarking-neural-network-robustness-common/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Benchmarking Neural Network Robustness to Common…](https://md.research.elamur.ai/free/en/papers/hendrycks-2019-benchmarking-neural-network-robustness-common/) / cited by # Who cites «Benchmarking Neural Network Robustness to Common Corruptions and Perturbations» 143 papers of our library cite it (of the 149,439 whose bibliographies we read) · 14 with a page here, the most cited first · [← back to «Benchmarking Neural Network Robustness to Common Corruptions and Perturbations»](https://md.research.elamur.ai/free/en/papers/hendrycks-2019-benchmarking-neural-network-robustness-common/) 1. [**Learning Transferable Visual Models From Natural Language Supervision arXiv:2103.00020**Alec Radford, Jong Wook Kim, Chris Hallacy, et al.*2021 · 8,767 citations*](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/) 2. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/) 3. [**DINOv2: Learning Robust Visual Features without Supervision arXiv:2304.07193**Maxime Oquab, Timothee Darcet, Theo Moutakanni, et al.*2023 · 1,513 citations*](https://md.research.elamur.ai/free/en/papers/oquab-2023-dinov2-learning-robust-visual-features/) 4. **[The Rise and Potential of Large Language Model Based Agents: A Survey](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Zhiheng Xi, Wenxiang Chen, Xin Guo, et al.*2023 · 1,272 citations* 5. **[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* 6. [**Masked Autoencoders Are Scalable Vision Learners arXiv:2111.06377**Kaiming He, Xinlei Chen, Saining Xie, et al.*2021 · 596 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-masked-autoencoders-scalable-vision-learners/) 7. **[Pretrained Transformers Improve Out-of-Distribution Robustness](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-pretrained-transformers-improve-out-distribution/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, et al.*2020 · 452 citations* 8. [**Aligning AI With Shared Human Values arXiv:2008.02275**Dan Hendrycks, Collin Burns, Steven Basart, et al.*2020 · 395 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-aligning-ai-shared-human-values/) 9. [**Unsolved Problems in ML Safety arXiv:2109.13916**Dan Hendrycks, Nicholas Carlini, John Schulman, et al.*2021 · 151 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-unsolved-problems-ml-safety/) 10. [**Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities arXiv:2408.07666**Enneng Yang, Li Shen, Guibing Guo, et al.*2024 · 79 citations*](https://md.research.elamur.ai/free/en/papers/yang-2024-model-merging-llms-mllms-methods/) 11. [**AdaMerging: Adaptive Model Merging for Multi-Task Learning arXiv:2310.02575**Enneng Yang, Zhenyi Wang, Li Shen, et al.*2023 · 59 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-adamerging-adaptive-model-merging-multi/) 12. **[RotBench: Evaluating Multi-modal Large Language Models on Identifying Image Rotation](https://md.research.elamur.ai/free/en/papers/niu-2026-rotbench-evaluating-multi-modal-large/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Tianyi Niu, Jaemin Cho, Elias Stengel-Eskin, et al.*2026 · 1 citation* 13. **[How Do Inpainting Artifacts Propagate to Language?](https://md.research.elamur.ai/free/en/papers/yashwante-2026-how-do-inpainting-artifacts-propagate/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pratham Yashwante, Davit Abrahamyan, Shresth Grover, et al.*2026* 14. **[When Relations Break: Analyzing Relation Hallucination in Vision-Language Model Under Rotation and Noise](https://md.research.elamur.ai/free/en/papers/shin-2026-when-relations-break-analyzing-relation/) [Workshop on Advances in Language and Vision Research (ALVR)](https://md.research.elamur.ai/free/en/venues/alvr/)**Philip Wootaek Shin, Ajay Narayanan Sridhar, Lakshmi Sivani Devarapalli, et al.*2026* 129 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/hendrycks-2019-benchmarking-neural-network-robustness-common/) — Benchmarking Neural Network Robustness to Common Corruptions and Perturbations