Robert Feldhans

According to our database, Robert Feldhans authored at least 8 papers between 2021 and 2023.

Collaborative distances:

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

2023

Contrasting Explanations for Understanding and Regularizing Model Adaptations.

André Artelt, Fabian Hinder, Valerie Vaquet, Robert Feldhans, Barbara Hammer

Neural Process. Lett., October, 2023

Generating Cardiovascular Data to Improve Training of Assistive Heart Devices.

Johannes Kummert, Alexander Schulz, Robert Feldhans, Moriz Habigt, Maike Stemmler, Christina Kohler, Dirk Abel, Rolf Rossaint, Barbara Hammer

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2023

Data Augmentation for Cardiovascular Time Series Data Using WaveNet.

Robert Feldhans, Alexander Schulz, Johannes Kummert, Moriz Habigt, Maike Stemmler, Christina Kohler, Dirk Abel, Rolf Rossaint, Barbara Hammer

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2023

Measuring Fairness with Biased Data: A Case Study on the Effects of Unsupervised Data in Fairness Evaluation.

Sarah Schröder, Alexander Schulz, Ivan Tarakanov, Robert Feldhans, Barbara Hammer

Proceedings of the Advances in Computational Intelligence, 2023

2022

The SAME score: Improved cosine based bias score for word embeddings.

Sarah Schröder, Alexander Schulz, Philip Kenneweg, Robert Feldhans, Fabian Hinder, Barbara Hammer

CoRR, 2022

2021

Evaluating Metrics for Bias in Word Embeddings.

Sarah Schröder, Alexander Schulz, Philip Kenneweg, Robert Feldhans, Fabian Hinder, Barbara Hammer

CoRR, 2021

Contrastive Explanations for Explaining Model Adaptations.

André Artelt, Fabian Hinder, Valerie Vaquet, Robert Feldhans, Barbara Hammer

Proceedings of the Advances in Computational Intelligence, 2021

Drift Detection in Text Data with Document Embeddings.

Robert Feldhans, Adrian Wilke, Stefan Heindorf, Mohammad Hossein Shaker, Barbara Hammer, Axel-Cyrille Ngonga Ngomo, Eyke Hüllermeier

Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2021, 2021