Pre-PhD

2019

Knauer, U., Styp von Rekowski, C., Stecklina, M., Krokotsch, T., Pham Minh, T., Hauffe, V., … & Seiffert, U. (2019). Tree species classification based on hybrid ensembles of a convolutional neural network (CNN) and random forest classifiers. Remote Sensing, 11(23), 2788.

Krokotsch, T., & Böck, R. (2019). Generative Adversarial Networks and Simulated + Unsupervised Learning in Affect Recognition from Speech. In 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE.

PhD

2020

Krokotsch, T., Knaak, M., & Gühmann, C. (2020). A Novel Evaluation Framework for Unsupervised Domain Adaption on Remaining Useful Lifetime Estimation. In 2020 IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE.

2021

Krokotsch, T., Knaak, M., & Gühmann, C. (2021). Improving Semi-Supervised Learning for Remaining Useful Lifetime Estimation Through Self-Supervision. arXiv preprint arXiv:2108.08721.