| 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, ... | 16 | 2018 |
| Deep convolutional recurrent neural network for rare acoustic event detection S Amiriparian, N Cummins, S Julka, B Schuller Proc. DAGA, 1522-1525, 2018 | 13 | 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 | 7 | 2021 |
| Conditional generative adversarial networks for speed control in trajectory simulation S Julka, V Sowrirajan, J Schloetterer, M Granitzer Machine Learning, Optimization, and Data Science: 7th International …, 2022 | 4 | 2022 |
| 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, ... arXiv preprint arXiv:2010.15996, 2020 | 4 | 2020 |
| Automatic detection of bow shock and magnetopause boundaries at Mercury using MESSENGER magnetometer data D Nevskii, D Parunakian, A Lavrukhin, S Julka, M Granitzer 44th COSPAR Scientific Assembly. Held 16-24 July 44, 475, 2022 | | 2022 |
| Machine Learning, Optimization, and Data Science: 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part II G Nicosia, V Ojha, E La Malfa, G La Malfa, G Jansen, PM Pardalos, ... Springer Nature, 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 |
| 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 | | 2021 |
| 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 | | 2021 |
| Automatic detection of magnetopause and bow shock crossing signatures in MESSENGER magnetometer data A Lavrukhin, D Parunakian, D Nevskiy, U Amerstorfer, A Windisch, ... European Planetary Science Congress, EPSC2020-826, 2020 | | 2020 |
| 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 |
| Deep Active Learning for Detection of Mercury’s Bow Shock and Magnetopause Crossings S Julka, N Kirschstein, M Granitzer, A Lavrukhin, U Amerstorfer | | |
| 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
