( en | de | ru )

 

Disambiguation

From post

копироватьудалитьдобавить публикацию в буфер
EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPy

, , и . (2020)cite arxiv:2008.04175.

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.

Martin Rauber

Herbert Räuber

Klaus Rauber

Julius Rauber

Lucio Rauber

 

Другие публикации лиц с тем же именем

EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPy, , и . (2020)cite arxiv:2008.04175.Scaling up the Randomized Gradient-Free Adversarial Attack Reveals Overestimation of Robustness Using Established Attacks., , и . Int. J. Comput. Vis., 128 (4): 1028-1046 (2020)Robust Perception through Analysis by Synthesis., , , и . CoRR, (2018)Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models., , и . CoRR, (2017)Advances in Reliably Evaluating and Improving Adversarial Robustness.. University of Tübingen, Germany, (2021)Generalisation in humans and deep neural networks, , , , , и . Advances in Neural Information Processing Systems, стр. 7549--7561. (2018)Accurate, reliable and fast robustness evaluation., , , , и . NeurIPS, стр. 12841-12851. (2019)Generalisation in humans and deep neural networks., , , , , и . NeurIPS, стр. 7549-7561. (2018)Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models., , и . ICLR (Poster), OpenReview.net, (2018)Foolbox: A Python toolbox to benchmark the robustness of machine learning models, , и . (2017)cite arxiv:1707.04131Comment: Code and examples available at https://github.com/bethgelab/foolbox and documentation available at http://foolbox.readthedocs.io.