Hendrycks, Dan

Gaussian Error Linear Units (GELUs)

Hendrycks, D., & Gimpel, K. (2016) · arXiv

Neural Networks and ApplicationsAdvanced Neural Network Applications

Cited by 3,537

A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Hendrycks, D., & Gimpel, K. (2016) · arXiv

Adversarial Robustness in Machine LearningMachine Learning and Data Classification

Cited by 1,968

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Hendrycks, D., & Dietterich, T. (2019) · arXiv

Smart Agriculture and AIAdvanced Neural Network Applications

Cited by 1,335

The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization

Hendrycks, D., Basart, S., Mu, N., Kadavath, S., Wang, F., Dorundo, E., Desai, R., Zhu, T., Parajuli, S., Guo, M., et al. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Domain Adaptation and Few-Shot LearningAdversarial Robustness in Machine Learning

Cited by 1,250

Natural Adversarial Examples

Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., & Song, D. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Adversarial Robustness in Machine LearningMachine Learning and Data Classification

Cited by 1,090

Natural Adversarial Examples

Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., & Song, D. (2019) · arXiv

Adversarial Robustness in Machine LearningMachine Learning and Data Classification

Cited by 1,058

Deep Anomaly Detection with Outlier Exposure

Hendrycks, D., Mazeika, M., & Dietterich, T. (2018) · arXiv

Anomaly Detection Techniques and ApplicationsAdversarial Robustness in Machine Learning

Cited by 932

Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

Hendrycks, D., Mazeika, M., Kadavath, S., & Song, D. (2019) · arXiv

Adversarial Robustness in Machine LearningDomain Adaptation and Few-Shot Learning

Cited by 634

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

Aligning AI With Shared Human Values

Hendrycks, D., Burns, C., Basart, S., Critch, A., Li, J., Song, D., & Steinhardt, J. (2020) · arXiv

Ethics and Social Impacts of AIPsychology of Moral and Emotional Judgment

Cited by 517

CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review

Hendrycks, D., Burns, C., Chen, A., & Ball, S. (2021) · arXiv

Artificial Intelligence in LawTopic Modeling

Cited by 308

Measuring Mathematical Problem Solving With the MATH Dataset

Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., & Steinhardt, J. (2021) · arXiv

Topic ModelingMathematics, Computing, and Information Processing

Cited by 306

Measuring Massive Multitask Language Understanding

Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., & Steinhardt, J. (2020) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 291

AI Deception: A Survey of Examples, Risks, and Potential Solutions

Park, P. S., Goldstein, S., O'Gara, A., Chen, M., & Hendrycks, D. (2023) · arXiv

Ethics and Social Impacts of AIMisinformation and Its Impacts

Cited by 243

AI deception: A survey of examples, risks, and potential solutions

Park, P. S., Goldstein, S., O’Gara, A., Chen, M., & Hendrycks, D. (2024) · Patterns

Ethics and Social Impacts of AIMisinformation and Its Impacts

Cited by 243

A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges

Salehi, M., Mirzaei, H., Hendrycks, D., Li, Y., Rohban, M. H., & Sabokrou, M. (2021) · arXiv

Anomaly Detection Techniques and ApplicationsAdversarial Robustness in Machine Learning

Cited by 118

Unsolved Problems in ML Safety

Hendrycks, D., Carlini, N., Schulman, J., & Steinhardt, J. (2021) · arXiv

Ethics and Social Impacts of AISafety Systems Engineering in Autonomy

Cited by 95

The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning

Li, N., Pan, A., Gopal, A., Yue, S., Berrios, D., Gatti, A., Li, J. D., Dombrowski, A.-K., Goel, S., Phan, L., et al. (2024) · arXiv

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 90

Representation Engineering: A Top-Down Approach to AI Transparency

Zou, A., Phan, L., Chen, S., Campbell, J., Guo, P., Ren, R., Pan, A., Yin, X., Mazeika, M., Dombrowski, A.-K., et al. (2023) · arXiv

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 65

Humanity's Last Exam

Phan, L., Gatti, A., Han, Z., Li, N., Hu, J., Zhang, H., Zhang, C. B. C., Shaaban, M., Ling, J., Shi, S., et al. (2025) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 65

Identifying and Mitigating the Security Risks of Generative AI

Barrett, C., Boyd, B., Bursztein, E., Carlini, N., Chen, B., Choi, J., Chowdhury, A. R., Christodorescu, M., Datta, A., Feizi, S., et al. (2023) · Foundations and Trends® in Privacy and Security

Adversarial Robustness in Machine LearningInformation and Cyber Security

Cited by 57

LLM-PBE: Assessing Data Privacy in Large Language Models

Li, Q., Hong, J., Xie, C., Tan, J., Xin, R., Hou, J., Yin, X., Wang, Z., Hendrycks, D., Wang, Z., et al. (2024) · Proceedings of the VLDB Endowment

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 42

LLM-PBE: Assessing Data Privacy in Large Language Models

Li, Q., Hong, J., Xie, C., Tan, J., Xin, R., Hou, J., Yin, X., Wang, Z., Hendrycks, D., Wang, Z., et al. (2024) · arXiv

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 42

Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark

Pan, A., Chan, J. S., Zou, A., Li, N., Basart, S., Woodside, T., Ng, J., Zhang, H., Emmons, S., & Hendrycks, D. (2023) · arXiv

Ethics and Social Impacts of AIEthics in Business and Education

Cited by 32