| Decoupling sparsity and smoothness in the dirichlet variational autoencoder topic model S Burkhardt, S Kramer Journal of Machine Learning Research 20 (131), 1-27, 2019 | 95 | 2019 |
| Online multi-label dependency topic models for text classification S Burkhardt, S Kramer Machine Learning 107, 859-886, 2018 | 63 | 2018 |
| Making thermodynamic models of mixtures predictive by machine learning: matrix completion of pair interactions F Jirasek, R Bamler, S Fellenz, M Bortz, M Kloft, S Mandt, H Hasse Chemical Science 13 (17), 4854-4862, 2022 | 23 | 2022 |
| A survey of multi-label topic models S Burkhardt, S Kramer ACM SIGKDD Explorations Newsletter 21 (2), 61-79, 2019 | 22 | 2019 |
| On the challenges and opportunities in generative ai L Manduchi, K Pandey, R Bamler, R Cotterell, S Däubener, S Fellenz, ... arXiv preprint arXiv:2403.00025, 2024 | 17 | 2024 |
| Rule extraction from binary neural networks with convolutional rules for model validation S Burkhardt, J Brugger, N Wagner, Z Ahmadi, K Kersting, S Kramer Frontiers in artificial intelligence 4, 642263, 2021 | 13 | 2021 |
| Towards identifying drug side effects from social media using active learning and crowd sourcing S Burkhardt, J Siekiera, J Glodde, MA Andrade-Navarro, S Kramer PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020, 319-330, 2019 | 13 | 2019 |
| Focusing knowledge-based graph argument mining via topic modeling P Abels, Z Ahmadi, S Burkhardt, B Schiller, I Gurevych, S Kramer arXiv preprint arXiv:2102.02086, 2021 | 12 | 2021 |
| Multi-label classification using stacked hierarchical Dirichlet processes with reduced sampling complexity S Burkhardt, S Kramer Knowledge and Information Systems 59, 93-115, 2019 | 12 | 2019 |
| Semisupervised bayesian active learning for text classification S Burkhardt, J Siekiera, S Kramer Bayesian Deep Learning Workshop at NeurIPS, 2018 | 12 | 2018 |
| Deep anomaly detection on Tennessee Eastman process data F Hartung, BJ Franks, T Michels, D Wagner, P Liznerski, S Reithermann, ... Chemie Ingenieur Technik 95 (7), 1077-1082, 2023 | 9 | 2023 |
| A call for standardization and validation of text style transfer evaluation P Ostheimer, M Nagda, M Kloft, S Fellenz arXiv preprint arXiv:2306.00539, 2023 | 9 | 2023 |
| HANNA: hard-constraint neural network for consistent activity coefficient prediction T Specht, M Nagda, S Fellenz, S Mandt, H Hasse, F Jirasek Chemical Science 15 (47), 19777-19786, 2024 | 8 | 2024 |
| On the spectrum between binary relevance and classifier chains in multi-label classification S Burkhardt, S Kramer Proceedings of the 30th Annual ACM Symposium on Applied Computing, 885-892, 2015 | 7 | 2015 |
| Text style transfer evaluation using large language models P Ostheimer, M Nagda, M Kloft, S Fellenz arXiv preprint arXiv:2308.13577, 2023 | 6 | 2023 |
| Topic-guided knowledge graph construction for argument mining W Li, P Abels, Z Ahmadi, S Burkhardt, B Schiller, I Gurevych, S Kramer 2021 IEEE International Conference on Big Knowledge (ICBK), 315-322, 2021 | 6 | 2021 |
| Online Sparse Collapsed Hybrid Variational-Gibbs Algorithm for Hierarchical Dirichlet Process Topic Models S Burkhardt, S Kramer ECML PKDD, 2017 | 5 | 2017 |
| Reimagining Anomalies: What If Anomalies Were Normal? P Liznerski, S Varshneya, E Calikus, S Fellenz, M Kloft arXiv preprint arXiv:2402.14469, 2024 | 4 | 2024 |
| Discriminative machine learning for maximal representative subsampling T Hauptmann, S Fellenz, L Nathan, O Tüscher, S Kramer Scientific reports 13 (1), 20925, 2023 | 2 | 2023 |
| Ordinal regression for difficulty prediction of StepMania levels BJ Franks, B Dinkelmann, M Kloft, S Fellenz Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023 | 2 | 2023 |
Stefan KramerProfessor of Computer Science, Johannes Gutenberg University MainzAdresse e-mail validée de informatik.uni-mainz.de
