| Gaussian Error Linear Units (GELUs) D Hendrycks, K Gimpel arXiv preprint arXiv:1606.08415, 2016 | 4892 | 2016 |
| Benchmarking Neural Network Robustness to Common Corruptions and Perturbations D Hendrycks, T Dietterich International Conference on Learning Representations (ICLR), 2019 | 3240 | 2019 |
| A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks D Hendrycks, K Gimpel International Conference on Learning Representations (ICLR), 2017 | 3165 | 2017 |
| Deep Anomaly Detection with Outlier Exposure D Hendrycks, M Mazeika, T Dietterich International Conference on Learning Representations (ICLR), 2019 | 1454 | 2019 |
| AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty D Hendrycks, N Mu, ED Cubuk, B Zoph, J Gilmer, B Lakshminarayanan International Conference on Learning Representations (ICLR), 2020 | 1306 | 2020 |
| The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization D Hendrycks, S Basart, N Mu, S Kadavath, F Wang, E Dorundo, R Desai, ... International Conference on Computer Vision (ICCV), 2020 | 1227 | 2020 |
| Natural Adversarial Examples D Hendrycks, K Zhao, S Basart, J Steinhardt, D Song Conference on Computer Vision and Pattern Recognition (CVPR), 2019 | 1156 | 2019 |
| Measuring Massive Multitask Language Understanding D Hendrycks, C Burns, S Basart, A Zou, M Mazeika, D Song, J Steinhardt International Conference on Learning Representations (ICLR), 2020 | 1064 | 2020 |
| Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty D Hendrycks, M Mazeika, S Kadavath, D Song Neural Information Processing Systems (NeurIPS), 2019 | 955 | 2019 |
| Using Pre-training Can Improve Model Robustness and Uncertainty D Hendrycks, K Lee, M Mazeika International Conference on Machine Learning, 2712-2721, 2019 | 746 | 2019 |
| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022 | 721 | 2022 |
| Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise D Hendrycks, M Mazeika, D Wilson, K Gimpel Neural Information Processing Systems (NeurIPS), 2018 | 591 | 2018 |
| Measuring Mathematical Problem Solving With the MATH Dataset D Hendrycks, C Burns, S Kadavath, A Arora, S Basart, E Tang, D Song, ... NeurIPS, 2021 | 476 | 2021 |
| Pretrained Transformers Improve Out-of-Distribution Robustness D Hendrycks, X Liu, E Wallace, A Dziedzic, R Krishnan, D Song Association for Computational Linguistics (ACL), 2020 | 406 | 2020 |
| Scaling Out-of-Distribution Detection for Real-World Settings D Hendrycks, S Basart, M Mazeika, M Mostajabi, J Steinhardt, D Song International Conference on Machine Learning (ICML), 2022 | 361* | 2022 |
| Early Methods for Detecting Adversarial Images D Hendrycks, K Gimpel International Conference on Learning Representations (ICLR) Workshop, 2017 | 303 | 2017 |
| Measuring Coding Challenge Competence With APPS D Hendrycks, S Basart, S Kadavath, M Mazeika, A Arora, E Guo, C Burns, ... NeurIPS, 2021 | 288 | 2021 |
| Aligning AI With Shared Human Values D Hendrycks, C Burns, S Basart, A Critch, J Li, D Song, J Steinhardt International Conference on Learning Representations (ICLR), 2020 | 250 | 2020 |
| Unsolved Problems in ML Safety D Hendrycks, N Carlini, J Schulman, J Steinhardt arXiv preprint arXiv:2109.13916, 2021 | 228 | 2021 |
| Testing robustness against unforeseen adversaries D Kang, Y Sun, D Hendrycks, T Brown, J Steinhardt | 178* | 2019 |
Dawn SongProfessor of Computer Science, UC BerkeleyE-mailová adresa ověřena na: cs.berkeley.edu
