Dan Hendrycks
Dan Hendrycks
PhD Student, UC Berkeley
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
1463*2016
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
D Hendrycks, K Gimpel
International Conference on Learning Representations (ICLR), 2017
6792017
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
D Hendrycks, T Dietterich
International Conference on Learning Representations (ICLR), 2019
5302019
Deep Anomaly Detection with Outlier Exposure
D Hendrycks, M Mazeika, T Dietterich
International Conference on Learning Representations (ICLR), 2019
2852019
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
1662018
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
D Hendrycks, M Mazeika, S Kadavath, D Song
Neural Information Processing Systems (NeurIPS), 2019
1482019
Early Methods for Detecting Adversarial Images
D Hendrycks, K Gimpel
International Conference on Learning Representations (ICLR) Workshop, 2017
147*2017
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
1402020
Using Pre-training Can Improve Model Robustness and Uncertainty
D Hendrycks, K Lee, M Mazeika
International Conference on Machine Learning, 2712-2721, 2019
1372019
Natural Adversarial Examples
D Hendrycks, K Zhao, S Basart, J Steinhardt, D Song
Conference on Computer Vision and Pattern Recognition (CVPR 2021), 2019
1152019
Testing robustness against unforeseen adversaries
D Kang, Y Sun, D Hendrycks, T Brown, J Steinhardt
arXiv preprint arXiv:1908.08016, 2019
52*2019
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, ...
arXiv preprint arXiv:2006.16241, 2020
382020
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
332020
Open Category Detection with PAC Guarantees
S Liu, R Garrepalli, TG Dietterich, A Fern, D Hendrycks
International Conference on Machine Learning (ICML), 2018
322018
Scaling Out-of-Distribution Detection for Real-World Settings
D Hendrycks, S Basart, M Mazeika, M Mostajabi, J Steinhardt, D Song
arXiv preprint arXiv:1911.11132, 2019
18*2019
Adjusting for Dropout Variance in Batch Normalization and Weight Initialization
D Hendrycks, K Gimpel
arXiv preprint arXiv:1607.02488, 2016
15*2016
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
72020
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
52020
A Discussion of'Adversarial Examples Are Not Bugs, They Are Features': Adversarial Example Researchers Need to Expand What is Meant by'Robustness'
J Gilmer, D Hendrycks
Distill 4 (8), e00019. 1, 2019
52019
Measuring mathematical problem solving with the math dataset
D Hendrycks, C Burns, S Kadavath, A Arora, S Basart, E Tang, D Song, ...
arXiv preprint arXiv:2103.03874, 2021
22021
The system can't perform the operation now. Try again later.
Articles 1–20