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 (ECML/PKDD (3), 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)

Deep Semi-supervised Learning for Time Series Classification. - Jann Goschenhofer; Rasmus Hvingelby; David Rügamer; Janek Thomas; Moritz Wagner 001; Bernd Bischl (ICMLA, 2021)

Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection. - Emilio Dorigatti; Jann Goschenhofer; Benjamin Schubert; Mina Rezaei; Bernd Bischl (CoRR, 2022)

Towards Enhancing Deep Active Learning with Weak Supervision and Constrained Clustering. - Matthias Aßenmacher; Lukas Rauch; Jann Goschenhofer; Andreas Stephan; Bernd Bischl; Benjamin Roth 001; Bernhard Sick (IAL@PKDD/ECML, 2023)

ConstraintMatch for Semi-constrained Clustering. - Jann Goschenhofer; Bernd Bischl; Zsolt Kira (IJCNN, 2023)

Approximately Bayes-optimal pseudo-label selection. - Julian Rodemann; Jann Goschenhofer; Emilio Dorigatti; Thomas Nagler; Thomas Augustin 001 (UAI, 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; et al. (CoRR, 2023)

Approximate Bayes Optimal Pseudo-Label Selection. - Julian Rodemann; Jann Goschenhofer; Emilio Dorigatti; Thomas Nagler; Thomas Augustin 001 (CoRR, 2023)

Reducing the effort for data annotation: contributions to weakly supervised deep learning. - Jann Goschenhofer (2023)

Structured Verification of Machine Learning Models in Industrial Settings. - Sai Rahul Kaminwar; Jann Goschenhofer; Janek Thomas; Ingo Thon; Bernd Bischl (Big Data, 2023)