Nadia Burkart

According to our database, Nadia Burkart authored at least 10 papers between 2019 and 2022.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2022

Erklärbare Künstliche Intelligenz - Steigerung der Nachvollziehbarkeit überwachter maschineller Lernverfahren.

Nadia Burkart

PhD thesis, 2022

Validation of XAI explanations for multivariate time series classification in the maritime domain.

Manjunatha Veerappa, Mathias Anneken, Nadia Burkart, Marco F. Huber

J. Comput. Sci., 2022

Explainable AI: introducing trust and comprehensibility to AI engineering.

Nadia Burkart, Danilo Brajovic, Marco F. Huber

Autom., 2022

2021

A Survey on the Explainability of Supervised Machine Learning.

Nadia Burkart, Marco F. Huber

J. Artif. Intell. Res., 2021

Are you sure? Prediction revision in automated decision-making.

Nadia Burkart, Sebastian Robert, Marco F. Huber

Expert Syst. J. Knowl. Eng., 2021

A Step Towards Global Counterfactual Explanations: Approximating the Feature Space Through Hierarchical Division and Graph Search.

Maximilian Becker, Nadia Burkart, Pascal Birnstill, Jürgen Beyerer

Adv. Artif. Intell. Mach. Learn., 2021

2020

Supported Decision-Making by Explainable Predictions of Ship Trajectories.

Nadia Burkart, Marco F. Huber, Mathias Anneken

Proceedings of the 15th International Conference on Soft Computing Models in Industrial and Environmental Applications, 2020

Batch-wise Regularization of Deep Neural Networks for Interpretability.

Nadia Burkart, Philipp M. Faller, Elisabeth Peinsipp, Marco F. Huber

Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2020

Evaluation of Interpretable Association Rule Mining Methods on Time-Series in the Maritime Domain.

Manjunatha Veerappa, Mathias Anneken, Nadia Burkart

Proceedings of the Pattern Recognition. ICPR International Workshops and Challenges, 2020

2019

Forcing Interpretability for Deep Neural Networks through Rule-Based Regularization.

Nadia Burkart, Marco F. Huber, Phillip Faller

Proceedings of the 18th IEEE International Conference On Machine Learning And Applications, 2019