Explaining quantum circuits with Shapley values: towards explainable quantum machine learning.
Quantum Mach. Intell., June, 2025
Efficient solution of the number partitioning problem on a quantum annealer: a hybrid quantum-classical decomposition approach.
J. Heuristics, June, 2025
On the effects of biased quantum random numbers on the initialization of artificial neural networks.
Mach. Learn., 2024
Quantum Wave Function Collapse for Procedural Content Generation.
IEEE Computer Graphics and Applications, 2024
Feature selection on quantum computers.
Quantum Mach. Intell., June, 2023
Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems.
,
,
,
,
,
,
,
,
,
,
,
,
,
IEEE Trans. Knowl. Data Eng., 2023
A Decomposition Method for the Hybrid Quantum-Classical Solution of the Number Partitioning Problem.
CoRR, 2023
A Quantum Optimization Case Study for a Transport Robot Scheduling Problem.
CoRR, 2023
Explainable Quantum Machine Learning.
CoRR, 2023
Shapley Values with Uncertain Value Functions.
Proceedings of the Advances in Intelligent Data Analysis XXI, 2023
Representation of binary classification trees with binary features by quantum circuits.
Quantum, 2022
Wavelet-packets for deepfake image analysis and detection.
Mach. Learn., 2022
Quantum Feature Selection.
CoRR, 2022
Quantum Circuit Evolution on NISQ Devices.
Proceedings of the IEEE Congress on Evolutionary Computation, 2022
Calibrated Simplex Mapping Classification.
CoRR, 2021
CupNet - Pruning a Network for Geometric Data.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2021, 2021
Adaptive Sampling of Pareto Frontiers with Binary Constraints Using Regression and Classification.
Proceedings of the 25th International Conference on Pattern Recognition, 2020
Optimized data exploration applied to the simulation of a chemical process.
Comput. Chem. Eng., 2019
The Good, the Bad and the Ugly: Augmenting a Black-Box Model with Expert Knowledge.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019 - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, 2019