| Recognition of echolalic autistic child vocalisations utilising convolutional recurrent neural networks S Amiriparian, A Baird, S Julka, A Alcorn, S Ottl, S Petrović, E Ainger, ... | 23 | 2018 |
| Deep convolutional recurrent neural network for rare acoustic event detection S Amiriparian, N Cummins, S Julka, B Schuller Proc. DAGA, 1522-1525, 2018 | 16 | 2018 |
| Day-ahead forecasting of the percentage of renewables based on time-series statistical methods R Basmadjian, A Shaafieyoun, S Julka Energies 14 (21), 7443, 2021 | 14 | 2021 |
| Knowledge distillation with segment anything (sam) model for planetary geological mapping S Julka, M Granitzer International Conference on Machine Learning, Optimization, and Data Science …, 2023 | 13 | 2023 |
| Lessons learned from the 1st Ariel Machine Learning Challenge: Correcting transiting exoplanet light curves for stellar spots N Nikolaou, IP Waldmann, A Tsiaras, M Morvan, B Edwards, KH Yip, ... RAS Techniques and Instruments 2 (1), 695-709, 2023 | 12 | 2023 |
| Conditional generative adversarial networks for speed control in trajectory simulation S Julka, V Sowrirajan, J Schloetterer, M Granitzer International Conference on Machine Learning, Optimization, and Data Science …, 2021 | 5 | 2021 |
| Spatio-temporal machine learning analysis of social media data and refugee movement statistics C Havas, L Wendlinger, J Stier, S Julka, V Krieger, C Ferner, ... ISPRS International Journal of Geo-Information 10 (8), 498, 2021 | 4 | 2021 |
| Day-Ahead Forecasting of the Percentage of Renewables Based on Time-Series Statistical Methods. Energies 2021, 14, 7443 R Basmadjian, A Shaafieyoun, S Julka s Note: MDPI stays neutral with regard to jurisdictional claims in published …, 2021 | 3 | 2021 |
| Deep active learning for detection of mercury’s bow shock and magnetopause crossings S Julka, N Kirschstein, M Granitzer, A Lavrukhin, U Amerstorfer Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022 | 2 | 2022 |
| LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource Languages N Kholodna, S Julka, M Khodadadi, MN Gumus, M Granitzer arXiv preprint arXiv:2404.02261, 2024 | 1 | 2024 |
| An active learning approach for automatic detection of bow shock and magnetopause crossing signatures in Mercury's magnetosphere using MESSENGER magnetometer observations. S Julka Proceedings of the 2nd Machine Learning in Heliophysics, 8, 2022 | 1 | 2022 |
| Generative adversarial networks for automatic detection of mounds in digital terrain models (mars arabia terra) S Julka, M Granitzer, B De Toffoli, L Penasa, R Pozzobon, U Amerstorfer EGU General Assembly Conference Abstracts, EGU21-9188, 2021 | 1 | 2021 |
| Deep Active Learning with Concept Drifts for Detection of Mercury’s Bow Shock and Magnetopause Crossings S Julka, R Ishmukhametov, M Granitzer International Conference on Machine Learning, Optimization, and Data Science …, 2023 | | 2023 |
| Automatic detection of bow shock and magnetopause boundaries at Mercury using MESSENGER magnetometer data D Nevskii, A Lavrukhin, S Julka, D Parunakian, M Granitzer 44th COSPAR Scientific Assembly. Held 16-24 July 44, 475, 2022 | | 2022 |
| Determination of magnetopause and bow shock shape based on convolutional neural network modelling of MESSENGER data A Lavrukhin, D Parunakian, D Nevsky, S Julka, M Granitzer, A Windisch, ... European Planetary Science Congress, EPSC2021-651, 2021 | | 2021 |
| Echtzeit-Lagebild für effizientes Migrationsmanagement zur Gewährleistung humanitärer Sicherheit (HUMAN+); Teilvorhaben: Integrative Echtzeit Lage-und Vorhersagemodelle für … M Granitzer, S Julka, J Stier, L Wendlinger Universität Passau, 2020 | | 2020 |
| Generative Adversarial Networks for automatic detection of mounds in Mars Arabia Terra. S Julka, M Granitzer, B De Toffoli, L Penasa, R Pozzobon, U Amerstorfer | | |
Granitzer MichaelProfessor Computer Science, University PassauVerified email at uni-passau.de
