Stefan Oehmcke

Stefan Oehmcke

Assistant Professor


  1. Knowledge sharing for population based neural network training

    Oehmcke, Stefan & Kramer, O., 1 Jan 2018, KI 2018: Advances in Artificial Intelligence - 41st German Conference on AI, 2018, Proceedings. Turhan, A-Y. & Trollmann, F. (eds.). Springer Verlag, p. 258-269 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11117 LNAI).

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

  2. Event detection in marine time series data

    Oehmcke, Stefan, Zielinski, O. & Kramer, O., 1 Jan 2015, KI 2015: Advances in Artificial Intelligence - 38th Annual German Conference on AI, Proceedings. Hölldobler, S., Krötzsch, M., Rudolph, S. & Peñaloza, R. (eds.). Springer Verlag, p. 279-286 8 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9324).

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

  3. Recurrent neural networks and exponential PAA for virtual marine sensors

    Oehmcke, Stefan, Zielinski, O. & Kramer, O., 30 Jun 2017, 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 4459-4466 8 p. 7966421

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

  4. Direct training of dynamic observation noise with UMarineNet

    Oehmcke, Stefan, Zielinski, O. & Kramer, O., 1 Jan 2018, Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings. Kurkova, V., Hammer, B., Manolopoulos, Y., Iliadis, L. & Maglogiannis, I. (eds.). Springer Verlag, p. 123-133 11 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11139 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

  5. Manifold learning with iterative dimensionality photo-projection

    Luckehe, D., Oehmcke, Stefan & Kramer, O., 30 Jun 2017, 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 2555-2561 7 p. 7966167

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

  6. Published

    Deep learning enables image-based tree counting, crown segmentation and height prediction at national scale

    Li, Sizhuo, Brandt, Martin Stefan, Fensholt, Rasmus, Kariryaa, Ankit, Igel, Christian, Gieseke, Fabian Cristian, Nord-Larsen, Thomas, Oehmcke, Stefan, Carlsen, Ask Holm, Junttila, S., Tong, Xiaoye, d’Aspremont, A. & Ciais, P., 2023, In: PNAS Nexus. 2, 4, 16 p., pgad076.

    Research output: Contribution to journalJournal articleResearchpeer-review

  7. Published
  8. Published

    Magnitude and Uncertainty Pruning Criterion for Neural Networks

    Ko, V., Oehmcke, Stefan & Gieseke, Fabian Cristian, 2019, 2019 IEEE International Conference on Big Data, Big Data. Baru, C., Huan, J., Khan, L., Hu, X. T., Ak, R., Tian, Y., Barga, R., Zaniolo, C., Lee, K. & Ye, Y. F. (eds.). IEEE, p. 2317-2326 9005692. (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

  9. Published

    Remember to Correct the Bias When Using Deep Learning for Regression!

    Igel, Christian & Oehmcke, Stefan, 2023, In: KI - Kunstliche Intelligenz. 37, 1, p. 33-40

    Research output: Contribution to journalJournal articleResearchpeer-review

  10. Published

    Ensemble Learning for Semantic Segmentation of Ancient Maya Architectures

    Hellweg, Thorben, Oehmcke, Stefan, Kariryaa, Ankit, Gieseke, Fabian Cristian & Igel, Christian, 2022, Discover the Mysteries of the Maya: Selected Contributions from the Machine Learning Challenge & the Discovery Challenge Workshop, ECML PKDD 2021. Kocev, D., Simidjievski, N., Kostovska, A., Dimitrovski, I. & Kokalj, Ž. (eds.). Ljubljana: Jožef Stefan Institute , p. 13-19 7 p.

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

ID: 209373892