3.2k citations · 7k across the 61 of their papers we have counts for
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Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
Dan Hendrycks, Mantas Mazeika, Duncan Wilson +1
The growing importance of massive datasets used for deep learning makes robustness to label noise a critical property for classifiers to have. Sources of label noise include automa…
Early Methods for Detecting Adversarial Images
Dan Hendrycks, Kevin Gimpel
Many machine learning classifiers are vulnerable to adversarial perturbations. An adversarial perturbation modifies an input to change a classifier's prediction without causing the…
Adjusting for Dropout Variance in Batch Normalization and Weight Initialization
Dan Hendrycks, Kevin Gimpel
We show how to adjust for the variance introduced by dropout with corrections to weight initialization and Batch Normalization, yielding higher accuracy. Though dropout can preserv…
Gaussian Error Linear Units (GELUs)
Dan Hendrycks, Kevin Gimpel
We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU activation function is , where the standard Gaussian…