1 code implementation • 22 Jan 2023 • Raoul Heese, Thore Gerlach, Sascha Mücke, Sabine Müller, Matthias Jakobs, Nico Piatkowski
The resulting attributions can be interpreted as explanations for why a specific circuit works well for a given task, improving the understanding of how to construct parameterized (or variational) quantum circuits, and fostering their human interpretability in general.
Explainable Artificial Intelligence (XAI) Quantum Machine Learning
1 code implementation • 24 Mar 2022 • Sascha Mücke, Raoul Heese, Sabine Müller, Moritz Wolter, Nico Piatkowski
In machine learning, fewer features reduce model complexity.
1 code implementation • https://ieeexplore.ieee.org/document/8950672 2020 • Raju Ram, Sabine Müller, Franz-Josef Pfreundt, Nicolas R. Gauger, Janis Keuper
Reducing its computational complexity from cubic to quadratic allows an efficient strong scaling of Bayesian Optimization while outperforming the previous approach regarding optimization accuracy.
no code implementations • 24 Dec 2019 • Sabine Müller, Joachim Weickert, Norbert Graf
In our work we investigate the generalization behavior of deep neural networks in this scenario.
no code implementations • 5 Oct 2019 • Amirhossein Kardoost, Sabine Müller, Joachim Weickert, Margret Keuper
Our simple method can provide competitive results compared to the costly CNN-based methods with parameter tuning.
no code implementations • CVPR 2015 • Hosnieh Sattar, Sabine Müller, Mario Fritz, Andreas Bulling
Previous work on predicting the target of visual search from human fixations only considered closed-world settings in which training labels are available and predictions are performed for a known set of potential targets.