| Machine Learning: The Basics A Jung https://link.springer.com/book/10.1007/978-981-16-8193-6, 2022 | 197* | 2022 |
| Predictive maintenance of photovoltaic panels via deep learning T Huuhtanen, A Jung 2018 ieee data science workshop (dsw), 66-70, 2018 | 83 | 2018 |
| Graphical lasso based model selection for time series A Jung, G Hannak, N Goertz IEEE Signal Processing Letters 22 (10), 1781-1785, 2015 | 77 | 2015 |
| Joint channel estimation and activity detection for multiuser communication systems G Hannak, M Mayer, A Jung, G Matz, N Goertz 2015 IEEE International Conference on Communication Workshop (ICCW), 2086-2091, 2015 | 67 | 2015 |
| Hercules: Deep Hierarchical Attentive Multilevel Fusion Model With Uncertainty Quantification for Medical Image Classification M Abdar, MA Fahami, L Rundo, P Radeva, AF Frangi, UR Acharya, ... IEEE Transactions on Industrial Informatics 19 (1), 274-285, 2022 | 46 | 2022 |
| Classifying process instances using recurrent neural networks M Hinkka, T Lehto, K Heljanko, A Jung Business Process Management Workshops: BPM 2018 International Workshops …, 2019 | 44 | 2019 |
| Semi-supervised learning in network-structured data via total variation minimization A Jung, AO Hero III, AC Mara, S Jahromi, A Heimowitz, YC Eldar IEEE Transactions on Signal Processing 67 (24), 6256-6269, 2019 | 43 | 2019 |
| Automating root cause analysis via machine learning in agile software testing environments J Kahles, J Törrönen, T Huuhtanen, A Jung 2019 12th IEEE Conference on Software Testing, Validation and Verification …, 2019 | 41 | 2019 |
| On the minimax risk of dictionary learning A Jung, YC Eldar, N Görtz IEEE Transactions on Information Theory 62 (3), 1501-1515, 2016 | 40 | 2016 |
| Predicting power outages caused by extratropical storms R Tervo, I Láng, A Jung, A Mäkelä Natural Hazards and Earth System Sciences Discussions 2020, 1-26, 2020 | 37 | 2020 |
| When is network lasso accurate? A Jung, N Tran, A Mara Frontiers in Applied Mathematics and Statistics 3, 28, 2018 | 36 | 2018 |
| Learning the conditional independence structure of stationary time series: A multitask learning approach A Jung IEEE Transactions on Signal Processing 63 (21), 5677-5690, 2015 | 35 | 2015 |
| Localized linear regression in networked data A Jung, N Tran IEEE Signal Processing Letters 26 (7), 1090-1094, 2019 | 34 | 2019 |
| A fixed-point of view on gradient methods for big data A Jung Frontiers in Applied Mathematics and Statistics 3, 18, 2017 | 32 | 2017 |
| An information-theoretic approach to personalized explainable machine learning A Jung, PHJ Nardelli IEEE Signal Processing Letters 27, 825-829, 2020 | 29 | 2020 |
| Dynamic sparse subspace clustering for evolving high-dimensional data streams J Sui, Z Liu, L Liu, A Jung, X Li IEEE Transactions on Cybernetics 52 (6), 4173-4186, 2020 | 27 | 2020 |
| Semi-supervised learning via sparse label propagation A Jung, AO Hero III, A Mara, S Jahromi arXiv preprint arXiv:1612.01414, 2016 | 27 | 2016 |
| Domain adaptation for resume classification using convolutional neural networks L Sayfullina, E Malmi, Y Liao, A Jung Analysis of Images, Social Networks and Texts: 6th International Conference …, 2018 | 26 | 2018 |
| Unbiased estimation of a sparse vector in white Gaussian noise A Jung, Z Ben-Haim, F Hlawatsch, YC Eldar IEEE transactions on information theory 57 (12), 7856-7876, 2011 | 25 | 2011 |
| Compressive spectral estimation for nonstationary random processes A Jung, G Tauböck, F Hlawatsch IEEE Transactions on Information Theory 59 (5), 3117-3138, 2013 | 23 | 2013 |
Franz HlawatschInstitute of Telecommunications, TU WienE-mail megerősítve itt: tuwien.ac.at
