( en | de | ru )

 

Disambiguation of "Rauber, Jonas"

Author of the publication

copydeleteadd this publication to your clipboard
Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models.

, , and . CoRR, (2017)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Herbert Räuber

Martin Rauber

Klaus Rauber

Julius Rauber

Andreas Räuber

 

Other publications of authors with the same name

Towards the first adversarially robust neural network model on MNIST., , , and . ICLR (Poster), OpenReview.net, (2019)Robust Perception through Analysis by Synthesis., , , and . CoRR, (2018)Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models., , and . CoRR, (2017)Advances in Reliably Evaluating and Improving Adversarial Robustness.. University of Tübingen, Germany, (2021)Accurate, reliable and fast robustness evaluation., , , , and . NeurIPS, page 12841-12851. (2019)Generalisation in humans and deep neural networks., , , , , and . NeurIPS, page 7549-7561. (2018)Generalisation in humans and deep neural networks, , , , , and . Advances in Neural Information Processing Systems, page 7549--7561. (2018)Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models., , and . ICLR (Poster), OpenReview.net, (2018)Foolbox: A Python toolbox to benchmark the robustness of machine learning models, , and . (2017)cite arxiv:1707.04131Comment: Code and examples available at https://github.com/bethgelab/foolbox and documentation available at http://foolbox.readthedocs.io.Scaling up the Randomized Gradient-Free Adversarial Attack Reveals Overestimation of Robustness Using Established Attacks., , and . Int. J. Comput. Vis., 128 (4): 1028-1046 (2020)