Follow
Dan Hendrycks
Dan Hendrycks
Director of the Center for AI Safety
Verified email at berkeley.edu - Homepage
Title
Cited by
Cited by
Year
Gaussian Error Linear Units (GELUs)
D Hendrycks, K Gimpel
arXiv preprint arXiv:1606.08415, 2016
51392016
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
D Hendrycks, T Dietterich
International Conference on Learning Representations (ICLR), 2019
33242019
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
D Hendrycks, K Gimpel
International Conference on Learning Representations (ICLR), 2017
32492017
Deep Anomaly Detection with Outlier Exposure
D Hendrycks, M Mazeika, T Dietterich
International Conference on Learning Representations (ICLR), 2019
14932019
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
13432020
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
12742020
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
12242020
Natural Adversarial Examples
D Hendrycks, K Zhao, S Basart, J Steinhardt, D Song
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
12002019
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
D Hendrycks, M Mazeika, S Kadavath, D Song
Neural Information Processing Systems (NeurIPS), 2019
9762019
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
7742022
Using Pre-training Can Improve Model Robustness and Uncertainty
D Hendrycks, K Lee, M Mazeika
International Conference on Machine Learning, 2712-2721, 2019
7642019
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
6072018
Measuring Mathematical Problem Solving With the MATH Dataset
D Hendrycks, C Burns, S Kadavath, A Arora, S Basart, E Tang, D Song, ...
NeurIPS, 2021
5282021
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
4152020
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
377*2022
Early Methods for Detecting Adversarial Images
D Hendrycks, K Gimpel
International Conference on Learning Representations (ICLR) Workshop, 2017
3052017
Measuring Coding Challenge Competence With APPS
D Hendrycks, S Basart, S Kadavath, M Mazeika, A Arora, E Guo, C Burns, ...
NeurIPS, 2021
3032021
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
2842020
Unsolved Problems in ML Safety
D Hendrycks, N Carlini, J Schulman, J Steinhardt
arXiv preprint arXiv:2109.13916, 2021
2432021
Testing robustness against unforeseen adversaries
D Kang, Y Sun, D Hendrycks, T Brown, J Steinhardt
184*2019
The system can't perform the operation now. Try again later.
Articles 1–20