| Towards better understanding of gradient-based attribution methods for Deep Neural Networks M Ancona, E Ceolini, C Öztireli, M Gross International Conference on Learning Representations (ICLR 2018), 2018 | 1098 | 2018 |
| Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation M Ancona, C Öztireli, M Gross International Conference on Machine Learning (ICML 2019), 2019 | 247 | 2019 |
| Gradient-based attribution methods M Ancona, E Ceolini, C Öztireli, M Gross Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 169-191, 2019 | 154 | 2019 |
| Minetime insight: Visualizing meeting habits to promote informed scheduling decisions M Ancona, M Beyeler, M Gross, T Günther IEEE transactions on visualization and computer graphics 27 (3), 1986-1999, 2019 | 9 | 2019 |
| Shapley value as principled metric for structured network pruning M Ancona, C Öztireli, M Gross arXiv preprint arXiv:2006.01795, 2020 | 7 | 2020 |
| Techniques for understanding how trained neural networks operate AC Öztireli, M Gross, M Ancona US Patent 11,568,212, 2023 | 3 | 2023 |
| Attribution methods for interpreting and optimizing deep neural networks M Ancona ETH Zurich, 2020 | 1 | 2020 |
Cengiz ÖztireliUniversity of Cambridge and Google Inc.Email verificata su cam.ac.uk
