Publikationen

Non-Sequential Machine Learning Pipelines with pyWATTS
Heidrich, B.; Phipps, K.; Meisenbacher, S.; Turowski, M.; Neumann, O.; Mikut, R.; Hagenmeyer, V.
2023. Zenodo. doi:10.5281/zenodo.7740850

EasyMLServe: Easy Development of REST Machine Learning Services
Neumann, O.; Schilling, M.; Reischl, M.; Mikut, R.
2022, Dezember. 32. Workshop Computational Intelligence (2022), Berlin, Deutschland, 1.–2. Dezember 2022

A Computational Workflow for Interdisciplinary Deep Learning Projects utilizing bwHPC Infrastructure
Schilling, M.; Neumann, O.; Scherr, T.; Cui, H.; Popova, A. A.; Levkin, P. A.; Götz, M.; Reischl, M.
2022. Proceedings of the 7th bwHPC Symposium, 69–74, Kommunikations- und Informationszentrum (kiz). doi:10.18725/OPARU-46069

Enhancing anomaly detection methods for energy time series using latent space data representations
Turowski, M.; Heidrich, B.; Phipps, K.; Schmieder, K.; Neumann, O.; Mikut, R.; Hagenmeyer, V.
2022. e-Energy ’22: The Thirteenth ACM International Conference on Future Energy Systems, Virtual Event, 28 June 2022- 1 July 2022. Ed.: S. Lehnhoff, 208–227, Association for Computing Machinery (ACM). doi:10.1145/3538637.3538851

Automating Time Series Analysis Workflows with pyWATTS
Heidrich, B.; Phipps, K.; Neumann, O.; Meisenbacher, S.; Turowski, M.; Mikut, R.; Hagenmeyer, V.
2022, Juni. Helmholtz Artificial Intelligence Conference (Helmholtz AI 2022), Dresden, Deutschland, 2.–3. Juni 2022

ObiWan-Microbi and microbeSEG: Deep Learning Tools for Microbial Image Analysis
Neumann, O.; Seiffarth, J.; Scherr, T.; Wollenhaupt, B.; Scharr, H.; Kohlheyer, D.; Nöh, K.; Mikut, R.
2022, Mai 31. Helmholtz Imaging Conference (HIP 2022), Berlin, Deutschland, 31. Mai–1. Juni 2022

microbeSEG dataset (1.0) [Data set]
Scherr, T.; Seiffarth, J.; Wollenhaupt, B.; Neumann, O.; Schilling, M.; Kohlheyer, D.; Scharr, H.; Mikut, R.; Nöh, K.
2022, April 28. doi:10.5281/zenodo.6497715

Tuning a Distance-Prediction-Based Cell Segmentation
Scherr, T.; Löffler, K.; Schilling, M.; Neumann, O.; Mikut, R.
2022, März 28. 20th IEEE International Symposium on Biomedical Imaging (ISBI 2022), Kalkutta, Indien, 28.–31. März 2022

Transformer Neural Networks for Building Load Forecasting
Hertel, M.; Ott, S.; Neumann, O.; Schäfer, B.; Mikut, R.; Hagenmeyer, V.
2022. 36. Conference on Neural Information Processing Systems (NeurIPS 2022), Online, 28. November–10. Dezember 2023

microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation
Scherr, T.; Seiffarth, J.; Wollenhaupt, B.; Neumann, O.; Schilling, M. P.; Kohlheyer, D.; Scharr, H.; Nöh, K.; Mikut, R.
2022. (A. Imran, Hrsg.) PLOS ONE, 17 (11), Art.-Nr.: e0277601. doi:10.1371/journal.pone.0277601

EasyMLServe: Easy Deployment of REST Machine Learning Services
Neumann, O.; Schilling, M.; Reischl, M.; Mikut, R.
2022. Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022. Hrsg.: H. Schulte; F. Hoffmann; R. Mikut, 11–30, KIT Scientific Publishing

Evaluation of Transformer Architectures for Electrical Load Time-Series Forecasting
Hertel, M.; Ott, S.; Schäfer, B.; Mikut, R.; Hagenmeyer, V.; Neumann, O.
2022. Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022. Hrsg.: H. Schulte, F. Hoffmann; R. Mikut, 93–110, KIT Scientific Publishing

Ciscnet - a Single-Branch Cell Nucleus Instance Segmentation and Classification Network
Böhland, M.; Neumann, O.; Schilling, M. P.; Reischl, M.; Mikut, R.; Loffler, K.; Scherr, T.
2022. 2022 IEEE International Symposium on Biomedical Imaging Challenges (ISBIC), 1–5, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ISBIC56247.2022.9854734

Modeling and Generating Synthetic Anomalies for Energy and Power Time Series
Turowski, M.; Weber, M.; Neumann, O.; Heidrich, B.; Phipps, K.; Çakmak, H. K.; Mikut, R.; Hagenmeyer, V.
2022. e-Energy ’22: The Thirteenth ACM International Conference on Future Energy Systems, Virtual Event, 28 June 2022- 1 July 2022. Ed.: S. Lehnhoff, 471–484, Association for Computing Machinery (ACM). doi:10.1145/3538637.3539760

Automated Annotator Variability Inspection for Biomedical Image Segmentation
Schilling, M. P.; Scherr, T.; Munke, F. R.; Neumann, O.; Schutera, M.; Mikut, R.; Reischl, M.
2022. IEEE access, 10, 2753–2765. doi:10.1109/ACCESS.2022.3140378

A Computational Workflow for Interdisciplinary Deep Learning Projects utilizing bwHPC Infrastructure
Schilling, M. P.; Neumann, O.; Scherr, T.; Cui, H.; Popova, A. A.; Levkin, P. A.; Götz, M.; Reischl, M.
2021, November 8. 7th bwHPC Symposium (2021), Online, 8. November 2021

Smart Data Representations: Impact on the Accuracy of Deep Neural Networks
Neumann, O.; Turowski, M.; Ludwig, N.; Heidrich, B.; Hagenmeyer, V.; Mikut, R.
2021. Proceedings - 31. Workshop Computational Intelligence : Berlin, 25. - 26. November 2021. Hrsg.: H. Schulte; F. Hoffmann; R. Mikut, 113–130, KIT Scientific Publishing

pyWATTS: Python Workflow Automation Tool for Time Series
Heidrich, B.; Bartschat, A.; Turowski, M.; Neumann, O.; Phipps, K.; Meisenbacher, S.; Schmieder, K.; Ludwig, N.; Mikut, R.; Hagenmeyer, V.
2021. Cornell University