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Found 6 papers, 3 papers with code
Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning
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
Scalable Hyperparameter Optimization with Lazy Gaussian Processes
Reducing its computational complexity from cubic to quadratic allows an efficient strong scaling of Bayesian Optimization while outperforming the previous approach regarding optimization accuracy.
Robustness of Brain Tumor Segmentation
In our work we investigate the generalization behavior of deep neural networks in this scenario.
Object Segmentation Tracking from Generic Video Cues
Our simple method can provide competitive results compared to the costly CNN-based methods with parameter tuning.
Prediction of Search Targets From Fixations in Open-World Settings
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.
