Marco Ancona
Title
Cited by
Cited by
Year
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
4412018
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
812019
Gradient-based attribution methods
M Ancona, E Ceolini, C Öztireli, M Gross
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 169-191, 2019
292019
Enea Ceolini, A
M Ancona
Cengiz Öztireli, and Markus H. Gross. A unified view of gradient-based …, 2017
82017
Towards better understanding of gradient-based attribution methods for Deep Neural Networks. arXiv [cs. LG]. 2017
M Ancona, E Ceolini, C Öztireli, M Gross
Zrimec et al, 2019
52019
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, 2019
42019
Shapley Value as Principled Metric for Structured Network Pruning
M Ancona, C Öztireli, M Gross
arXiv preprint arXiv:2006.01795, 2020
22020
Towards Better Understanding of Gradient-based Attribution Methods for Deep Neural Networks
AC Oztireli, M Ancona, E Ceolini, M Gross
12019
Techniques for understanding how trained neural networks operate
A Öztireli, M Gross, M Ancona
US Patent App. 16/533,301, 2021
2021
Attribution Methods for Interpreting and Optimizing Deep Neural Networks
M Ancona
ETH Zurich, 2020
2020
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