🔗 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»

1. Learning Transferable Visual Models From Natural Language SupervisionarXiv:2103.00020Alec Radford, Jong Wook Kim, Chris Hallacy, et al.2021 · 8,767 citations
2. On the Opportunities and Risks of Foundation ModelsarXiv:2108.07258Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.2021 · 3,160 citations
3. DINOv2: Learning Robust Visual Features without SupervisionarXiv:2304.07193Maxime Oquab, Timothee Darcet, Theo Moutakanni, et al.2023 · 1,513 citations
4. The Rise and Potential of Large Language Model Based Agents: A SurveyScience China Information SciencesZhiheng Xi, Wenxiang Chen, Xin Guo, et al.2023 · 1,272 citations
5. Reproducible scaling laws for contrastive language-image learningIEEE Computer Society Conference on Computer Vision and Pattern RecognitionMehdi Cherti, Romain Beaumont, Ross Wightman, et al.2022 · 769 citations
6. Masked Autoencoders Are Scalable Vision LearnersarXiv:2111.06377Kaiming He, Xinlei Chen, Saining Xie, et al.2021 · 596 citations
7. Pretrained Transformers Improve Out-of-Distribution RobustnessAnnual Meeting of the Association for Computational Linguistics (ACL)Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, et al.2020 · 452 citations
8. Aligning AI With Shared Human ValuesarXiv:2008.02275Dan Hendrycks, Collin Burns, Steven Basart, et al.2020 · 395 citations
9. Unsolved Problems in ML SafetyarXiv:2109.13916Dan Hendrycks, Nicholas Carlini, John Schulman, et al.2021 · 151 citations
10. Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and OpportunitiesarXiv:2408.07666Enneng Yang, Li Shen, Guibing Guo, et al.2024 · 79 citations
11. AdaMerging: Adaptive Model Merging for Multi-Task LearningarXiv:2310.02575Enneng Yang, Zhenyi Wang, Li Shen, et al.2023 · 59 citations
12. RotBench: Evaluating Multi-modal Large Language Models on Identifying Image RotationConference of the European Chapter of the Association for Computational Linguistics (EACL)Tianyi Niu, Jaemin Cho, Elias Stengel-Eskin, et al.2026 · 1 citation
13. How Do Inpainting Artifacts Propagate to Language?Annual Meeting of the Association for Computational Linguistics (ACL)Pratham Yashwante, Davit Abrahamyan, Shresth Grover, et al.2026
14. When Relations Break: Analyzing Relation Hallucination in Vision-Language Model Under Rotation and NoiseWorkshop on Advances in Language and Vision Research (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.

This page in Markdown → https://md.research.elamur.ai/free/en/papers/hendrycks-2019-benchmarking-neural-network-robustness-common/cited-by/