Jann Goschenhofer

According to our database, Jann Goschenhofer authored at least 11 papers between 2019 and 2023.

Collaborative distances:

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

2023

Structured Verification of Machine Learning Models in Industrial Settings.

Sai Rahul Kaminwar, Jann Goschenhofer, Janek Thomas, Ingo Thon, Bernd Bischl

Big Data, June, 2023

Reducing the effort for data annotation: contributions to weakly supervised deep learning.

Jann Goschenhofer

PhD thesis, 2023

Approximate Bayes Optimal Pseudo-Label Selection.

Julian Rodemann, Jann Goschenhofer, Emilio Dorigatti, Thomas Nagler, Thomas Augustin

CoRR, 2023

Multimodal Deep Learning.

Cem Akkus, Luyang Chu, Vladana Djakovic, Steffen Jauch-Walser, Philipp Koch, Giacomo Loss, Christopher Marquardt, Marco Moldovan, Nadja Sauter, Maximilian Schneider, Rickmer Schulte, Karol Urbanczyk, Jann Goschenhofer, Christian Heumann, Rasmus Hvingelby, Daniel Schalk, Matthias Aßenmacher

CoRR, 2023

Approximately Bayes-optimal pseudo-label selection.

Julian Rodemann, Jann Goschenhofer, Emilio Dorigatti, Thomas Nagler, Thomas Augustin

Proceedings of the Uncertainty in Artificial Intelligence, 2023

ConstraintMatch for Semi-constrained Clustering.

Jann Goschenhofer, Bernd Bischl, Zsolt Kira

Proceedings of the International Joint Conference on Neural Networks, 2023

Towards Enhancing Deep Active Learning with Weak Supervision and Constrained Clustering.

Matthias Aßenmacher, Lukas Rauch, Jann Goschenhofer, Andreas Stephan, Bernd Bischl, Benjamin Roth, Bernhard Sick

Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2023), 2023

2022

Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection.

Emilio Dorigatti, Jann Goschenhofer, Benjamin Schubert, Mina Rezaei, Bernd Bischl

CoRR, 2022

2021

Deep Semi-supervised Learning for Time Series Classification.

Jann Goschenhofer, Rasmus Hvingelby, David Rügamer, Janek Thomas, Moritz Wagner, Bernd Bischl

Proceedings of the 20th IEEE International Conference on Machine Learning and Applications, 2021

2019

Granular Motor State Monitoring of Free Living Parkinson's Disease Patients via Deep Learning.

Kamer Ali Yuksel, Jann Goschenhofer, Hridya V. Varma, Urban Fietzek, Franz Michael Josef Pfister

CoRR, 2019

Wearable-Based Parkinson's Disease Severity Monitoring Using Deep Learning.

Jann Goschenhofer, Franz Michael Josef Pfister, Kamer Ali Yuksel, Bernd Bischl, Urban Fietzek, Janek Thomas

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019