| panelcn. MOPS: Copy‐number detection in targeted NGS panel data for clinical diagnostics G Povysil, A Tzika, J Vogt, V Haunschmid, L Messiaen, J Zschocke, ... Human mutation 38 (7), 889-897, 2017 | 85 | 2017 |
| A DaQL to monitor data quality in machine learning applications L Ehrlinger, V Haunschmid, D Palazzini, C Lettner Database and Expert Systems Applications: 30th International Conference …, 2019 | 51 | 2019 |
| Towards explainable music emotion recognition: The route via mid-level features S Chowdhury, A Vall, V Haunschmid, G Widmer arXiv preprint arXiv:1907.03572, 2019 | 47 | 2019 |
| Anomalous sound detection as a simple binary classification problem with careful selection of proxy outlier examples P Primus, V Haunschmid, P Praher, G Widmer arXiv preprint arXiv:2011.02949, 2020 | 36 | 2020 |
| audiolime: Listenable explanations using source separation V Haunschmid, E Manilow, G Widmer arXiv preprint arXiv:2008.00582, 2020 | 28 | 2020 |
| Emotion and theme recognition in music with frequency-aware RF-regularized CNNs K Koutini, S Chowdhury, V Haunschmid, H Eghbal-Zadeh, G Widmer arXiv preprint arXiv:1911.05833, 2019 | 23 | 2019 |
| On the veracity of local, model-agnostic explanations in audio classification: targeted investigations with adversarial examples V Praher, K Prinz, A Flexer, G Widmer arXiv preprint arXiv:2107.09045, 2021 | 14 | 2021 |
| Two-level Explanations in Music Emotion Recognition V Haunschmid, S Chowdhury, G Widmer arXiv preprint arXiv:1905.11760, 2019 | 13 | 2019 |
| Tracing back music emotion predictions to sound sources and intuitive perceptual qualities S Chowdhury, V Praher, G Widmer arXiv preprint arXiv:2106.07787, 2021 | 12 | 2021 |
| LEMONS: Listenable Explanations for Music recOmmeNder Systems AB Melchiorre, V Haunschmid, M Schedl, G Widmer ECIR 2021 12657, 2021 | 10 | 2021 |
| On data augmentation and adversarial risk: An empirical analysis H Eghbal-zadeh, K Koutini, P Primus, V Haunschmid, M Lewandowski, ... arXiv preprint arXiv:2007.02650, 2020 | 10 | 2020 |
| Anomalous sound detection with masked autoregressive flows and machine type dependent postprocessing V Haunschmid, P Praher Tech. Rep., DCASE2020 Challenge, 2020 | 9 | 2020 |
| Receptive-field regularized CNNs for music classification and tagging K Koutini, H Eghbal-Zadeh, V Haunschmid, P Primus, S Chowdhury, ... arXiv preprint arXiv:2007.13503, 2020 | 7 | 2020 |
| Concept-based techniques for" musicologist-friendly" explanations in a deep music classifier F Foscarin, K Hoedt, V Praher, A Flexer, G Widmer arXiv preprint arXiv:2208.12485, 2022 | 5 | 2022 |
| On the optimization of material usage in power transformer manufacturing G Chasparis, W Zellinger, V Haunschmid, M Riedenbauer, R Stumptner 2016 IEEE 8th International Conference on Intelligent Systems (IS), 680-685, 2016 | 5 | 2016 |
| Towards Musically Meaningful Explanations Using Source Separation V Haunschmid, E Manilow, G Widmer arXiv preprint arXiv:2009.02051, 2020 | 4 | 2020 |
| Constructing adversarial examples to investigate the plausibility of explanations in deep audio and image classifiers K Hoedt, V Praher, A Flexer, G Widmer Neural Computing and Applications 35 (14), 10011-10029, 2023 | 3 | 2023 |
| An evolutionary stochastic-local-search framework for one-dimensional cutting-stock problems GC Chasparis, M Rossbory, V Haunschmid arXiv preprint arXiv:1707.08776, 2017 | 2 | 2017 |
| panelcn. MOPS: CNV detection in targeted NGS panel data for clinical diagnostics G Povysil, A Tzika, J Vogt, V Haunschmid, L Messiaen, J Zschocke, ... EUROPEAN JOURNAL OF HUMAN GENETICS 26, 698-698, 2018 | | 2018 |
| panelcn. MOPS reaches clinical standards as a copy number variation detection tool for targeted panel sequencing/eingereicht von Verena Haunschmid BSc V Haunschmid Universität Linz, 0 | | |
Gerhard WidmerProfessor of Computer Science, Johannes Kepler University LinzVerified email at jku.at
