35 citations · 39 across the 4 of their papers we have counts for
4 papers · 1 filter
Towards Robust Explanations for Deep Neural Networks
Ann-Kathrin Dombrowski, Christopher J. Anders, Klaus-Robert Müller +1
Explanation methods shed light on the decision process of black-box classifiers such as deep neural networks. But their usefulness can be compromised because they are susceptible t…
Fairwashing Explanations with Off-Manifold Detergent
Christopher J. Anders, Plamen Pasliev, Ann-Kathrin Dombrowski +2
Explanation methods promise to make black-box classifiers more transparent. As a result, it is hoped that they can act as proof for a sensible, fair and trustworthy decision-making…
Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
Kim A. Nicoli, Christopher J. Anders, Lena Funcke +5
In this work, we demonstrate that applying deep generative machine learning models for lattice field theory is a promising route for solving problems where Markov Chain Monte Carlo…
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin +2
With the broader and highly successful usage of machine learning in industry and the sciences, there has been a growing demand for Explainable AI. Interpretability and explanation…