output
20172026
most citedA Survey on the Explainability of Supervised Machine Learning

984 citations

Showing 2025Show all

8 papers · 1 filter

cs.CV2025★ 3 cited

Synset Signset Germany: a Synthetic Dataset for German Traffic Sign Recognition

Anne Sielemann, Lena Loercher, Max-Lion Schumacher +3

In this paper, we present a synthesis pipeline and dataset for training / testing data in the task of traffic sign recognition that combines the advantages of data-driven and analy…

cs.LG2025

From Confusion to Clarity: ProtoScore -- A Framework for Evaluating Prototype-Based XAI

Helena Monke, Benjamin Sae-Chew, Benjamin Fresz +1

The complexity and opacity of neural networks (NNs) pose significant challenges, particularly in high-stakes fields such as healthcare, finance, and law, where understanding decisi…

quant-ph2025

Data-Efficient Quantum Noise Modeling via Machine Learning

Yanjun Ji, Marco Roth, David A. Kreplin +2

Maximizing the computational utility of near-term quantum processors requires predictive noise models that inform robust, noise-aware compilation and error mitigation. Conventional…

quant-ph2025★ 5 cited

Quantum vs. classical: A comprehensive benchmark study for predicting time series with variational quantum machine learning

Tobias Fellner, David Kreplin, Samuel Tovey +1

Variational quantum machine learning algorithms have been proposed as promising tools for time series prediction, with the potential to handle complex sequential data more effectiv…

cs.LG2025★ 3 cited

ML-Based Bidding Price Prediction for Pay-As-Bid Ancillary Services Markets: A Use Case in the German Control Reserve Market

Vincent Bezold, Lukas Baur, Alexander Sauer

The increasing integration of renewable energy sources has led to greater volatility and unpredictability in electricity generation, posing challenges to grid stability. Ancillary…

cs.ET2025★ 1 cited

Status and Future Prospects of the Standardization Framework Industry 4.0: A European Perspective

Olga Meyer, Marvin Boell, Christoph Legat

The rapid development of Industry 4.0 technologies requires robust and comprehensive standardization to ensure interoperability, safety and efficiency in the Industry of the Future…

quant-ph2025★ 4 cited

On the similarity of bandwidth-tuned quantum kernels and classical kernels

Roberto Flórez-Ablan, Marco Roth, Jan Schnabel

Quantum kernels (QK) are widely used in quantum machine learning applications; yet, their potential to surpass classical machine learning methods on classical datasets remains unce…

quant-ph2025★ 2 cited

AutoQML: A Framework for Automated Quantum Machine Learning

Marco Roth, David A. Kreplin, Daniel Basilewitsch +7

Automated Machine Learning (AutoML) has significantly advanced the efficiency of ML-focused software development by automating hyperparameter optimization and pipeline construction…