Dan Hendrycks

I am a first-year PhD student in computer science at UC Berkeley. I am interested in ML Safety through Uncertainty Estimation, Robustness, and Explainability. In 2018 I received my BS from UChicago. This summer I am interning at DeepMind. hendrycks ατ berkeley.edu

Works

• Natural Adversarial Examples; Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty; Towards Out-of-Distribution Detection for Large-Scale Images; Transfer of Robustness Against Adversarial and Stochastic Distortions
  In Submission (soon public)
  


• Using Pre-Training Can Improve Model Robustness and Uncertainty
  Dan Hendrycks, Kimin Lee, Mantas Mazeika
  ICML 2019
  [arXiv] [code]


• Deep Anomaly Detection with Outlier Exposure
  Dan Hendrycks, Mantas Mazeika, Thomas Dietterich
  ICLR 2019
  [arXiv] [code]


• Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
  Dan Hendrycks, Thomas Dietterich
  ICLR 2019
  [arXiv] [code]


• Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
  Dan Hendrycks*, Mantas Mazeika*, Duncan Wilson, Kevin Gimpel
  NeurIPS 2018
  [arXiv] [code]


• Open Category Detection with PAC Guarantees
  Si Liu, Risheek Garrepalli, Thomas G. Dietterich, Alan Fern, Dan Hendrycks
  ICML 2018
  [arXiv] [code]


• A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
  Dan Hendrycks, Kevin Gimpel
  ICLR 2017
  [arXiv] [code]


• Early Methods for Detecting Adversarial Images
  Dan Hendrycks, Kevin Gimpel
  ICLR 2017 Workshop
  [arXiv]


• Gaussian Error Linear Units (GELUs)
  Dan Hendrycks, Kevin Gimpel
  Technical Report, in which we propose the SiLU (appropriated as the swish activation) and the GELU, used in BERT and OpenAI GPT.
  [arXiv]

Service