Audio based bird species identification using deep learning techniques E Sprengel, M Jaggi, Y Kilcher, T Hofmann LifeCLEF 2016, 547-559, 2016 | 73 | 2016 |

The odds are odd: A statistical test for detecting adversarial examples K Roth, Y Kilcher, T Hofmann International Conference on Machine Learning, 5498-5507, 2019 | 42 | 2019 |

Scalable adaptive stochastic optimization using random projections G Krummenacher, B McWilliams, Y Kilcher, JM Buhmann, N Meinshausen arXiv preprint arXiv:1611.06652, 2016 | 16 | 2016 |

Semantic interpolation in implicit models Y Kilcher, A Lucchi, T Hofmann arXiv preprint arXiv:1710.11381, 2017 | 8 | 2017 |

Adversarial training generalizes data-dependent spectral norm regularization K Roth, Y Kilcher, T Hofmann | 3 | 2019 |

Meta answering for machine reading B Borschinger, J Boyd-Graber, C Buck, J Bulian, M Ciaramita, ... arXiv preprint arXiv:1911.04156, 2019 | 1 | 2019 |

Adversarial Training is a Form of Data-dependent Operator Norm Regularization K Roth, Y Kilcher, T Hofmann arXiv preprint arXiv:1906.01527, 2019 | 1 | 2019 |

The best defense is a good offense: Countering black box attacks by predicting slightly wrong labels Y Kilcher, T Hofmann arXiv preprint arXiv:1711.05475, 2017 | 1 | 2017 |

Parametrizing filters of a CNN with a GAN Y Kilcher, G Becigneul, T Hofmann arXiv preprint arXiv:1710.11386, 2017 | 1 | 2017 |

Generator Reversal Y Kilcher, A Lucchi, T Hofmann arXiv preprint arXiv:1707.09241, 2017 | 1 | 2017 |

How does BERT capture semantics? A closer look at polysemous words D Yenicelik, F Schmidt, Y Kilcher Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting …, 2020 | | 2020 |

Escaping Flat Areas via Function-Preserving Structural Network Modifications Y Kilcher, G Bécigneul, T Hofmann | | 2018 |

Flexible Prior Distributions for Deep Generative Models Y Kilcher, A Lucchi, T Hofmann arXiv preprint arXiv:1710.11383, 2017 | | 2017 |