How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks

16 June 2020

Papers citing "How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks"

7 / 7 papers shown

Title
Towards ML Methods for Biodiversity: A Novel Wild Bee Dataset and Evaluations of XAI Methods for ML-Assisted Rare Species Annotations Teodor Chiaburu F. Biessmann Frank Haußer 32 2 0 15 Jun 2022
Toward Explainable AI for Regression Models S. Letzgus Patrick Wagner Jonas Lederer Wojciech Samek Klaus-Robert Muller G. Montavon XAI 28 63 0 21 Dec 2021
On the use of uncertainty in classifying Aedes Albopictus mosquitoes Gereziher W. Adhane Mohammad Mahdi Dehshibi David Masip 21 7 0 29 Oct 2021
This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation Srishti Gautam Marina M.-C. Höhne Stine Hansen Robert Jenssen Michael C. Kampffmeyer 19 49 0 27 Aug 2021
Explainability of deep vision-based autonomous driving systems: Review and challenges Éloi Zablocki H. Ben-younes P. Pérez Matthieu Cord XAI 37 169 0 13 Jan 2021
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015