Dan Hendrycks
Dan Hendrycks
PhD Student, UC Berkeley
Dirección de correo verificada de berkeley.edu - Página principal
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Citado por
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Año
Gaussian Error Linear Units (GELUs)
D Hendrycks, K Gimpel
arXiv preprint arXiv:1606.08415, 2016
570*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
2742017
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
D Hendrycks, T Dietterich
International Conference on Learning Representations (ICLR), 2019
145*2019
Early Methods for Detecting Adversarial Images
D Hendrycks, K Gimpel
International Conference on Learning Representations (ICLR) Workshop, 2017
932017
Deep Anomaly Detection with Outlier Exposure
D Hendrycks, M Mazeika, T Dietterich
International Conference on Learning Representations (ICLR), 2019
832019
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
792018
Using pre-training can improve model robustness and uncertainty
D Hendrycks, K Lee, M Mazeika
arXiv preprint arXiv:1901.09960, 2019
362019
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
D Hendrycks, M Mazeika, S Kadavath, D Song
Neural Information Processing Systems (NeurIPS), 2019
252019
Testing robustness against unforeseen adversaries
D Kang, Y Sun, D Hendrycks, T Brown, J Steinhardt
arXiv preprint arXiv:1908.08016, 2019
18*2019
Natural adversarial examples
D Hendrycks, K Zhao, S Basart, J Steinhardt, D Song
arXiv preprint arXiv:1907.07174, 2019
162019
Open Category Detection with PAC Guarantees
S Liu, R Garrepalli, TG Dietterich, A Fern, D Hendrycks
International Conference on Machine Learning (ICML), 2018
152018
Adjusting for Dropout Variance in Batch Normalization and Weight Initialization
D Hendrycks, K Gimpel
arXiv preprint arXiv:1607.02488, 2016
11*2016
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
92020
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
22019
A Benchmark for Anomaly Segmentation
D Hendrycks, S Basart, M Mazeika, M Mostajabi, J Steinhardt, D Song
arXiv preprint arXiv:1911.11132, 2019
12019
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Artículos 1–15