| 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 | 122 | 2019 |
| Audio based bird species identification using deep learning techniques E Sprengel, M Jaggi, Y Kilcher, T Hofmann LifeCLEF 2016, 547-559, 2016 | 116 | 2016 |
| Adversarial training is a form of data-dependent operator norm regularization K Roth, Y Kilcher, T Hofmann Advances in Neural Information Processing Systems 33, 14973-14985, 2020 | 24 | 2020 |
| 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 | 18 | 2020 |
| Semantic interpolation in implicit models Y Kilcher, A Lucchi, T Hofmann arXiv preprint arXiv:1710.11381, 2017 | 18 | 2017 |
| Scalable adaptive stochastic optimization using random projections G Krummenacher, B McWilliams, Y Kilcher, JM Buhmann, N Meinshausen Advances in Neural Information Processing Systems 29, 2016 | 16 | 2016 |
| Boosting search engines with interactive agents L Adolphs, B Boerschinger, C Buck, MC Huebscher, M Ciaramita, ... arXiv preprint arXiv:2109.00527, 2021 | 5 | 2021 |
| Adversarial training generalizes data-dependent spectral norm regularization K Roth, Y Kilcher, T Hofmann | 5 | 2019 |
| Generative minimization networks: Training GANs without competition P Grnarova, Y Kilcher, KY Levy, A Lucchi, T Hofmann arXiv preprint arXiv:2103.12685, 2021 | 4 | 2021 |
| FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control D von Rütte, L Biggio, Y Kilcher, T Hoffman arXiv preprint arXiv:2201.10936, 2022 | 2 | 2022 |
| 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 | 2 | 2017 |
| Generator reversal Y Kilcher, A Lucchi, T Hofmann arXiv preprint arXiv:1707.09241, 2017 | 2 | 2017 |
| Meta answering for machine reading B Borschinger, J Boyd-Graber, C Buck, J Bulian, M Ciaramita, ... arXiv preprint arXiv:1911.04156, 2019 | 1 | 2019 |
| Escaping Flat Areas via Function-Preserving Structural Network Modifications Y Kilcher, G Bécigneul, T Hofmann | 1 | 2018 |
| Flexible Prior Distributions for Deep Generative Models Y Kilcher, A Lucchi, T Hofmann arXiv preprint arXiv:1710.11383, 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 |
| Boosting Search Engines with Interactive Agents B Boerschinger, CCF Buck, LJG Espeholt, L Adolphs, LS Saralegui, ... | | 2022 |
| Rethinking Neural Networks With Benford's Law SK Sahu, A Java, A Shaikh, Y Kilcher arXiv preprint arXiv:2102.03313, 2021 | | 2021 |
| Rethinking Neural Networks With Benford's Law S Kant Sahu, A Java, A Shaikh, Y Kilcher arXiv e-prints, arXiv: 2102.03313, 2021 | | 2021 |
| Navigating the Latent Spaces of Deep Neural Networks using Adversarial Techniques Y Kilcher ETH Zurich, 2021 | | 2021 |
