| An end-to-end deep learning architecture for graph classification M Zhang, Z Cui, M Neumann, Y Chen Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 1841 | 2018 |
| Tudataset: A collection of benchmark datasets for learning with graphs C Morris, NM Kriege, F Bause, K Kersting, P Mutzel, M Neumann arXiv preprint arXiv:2007.08663, 2020 | 816 | 2020 |
| Benchmark data sets for graph kernels K Kersting, NM Kriege, C Morris, P Mutzel, M Neumann | 297 | 2016 |
| Propagation kernels: efficient graph kernels from propagated information M Neumann, R Garnett, C Bauckhage, K Kersting Machine learning 102, 209-245, 2016 | 288 | 2016 |
| Erosion band features for cell phone image based plant disease classification M Neumann, L Hallau, B Klatt, K Kersting, C Bauckhage 2014 22nd International Conference on Pattern Recognition, 3315-3320, 2014 | 68 | 2014 |
| Efficient graph kernels by randomization M Neumann, N Patricia, R Garnett, K Kersting Machine Learning and Knowledge Discovery in Databases: European Conference …, 2012 | 67 | 2012 |
| Automated identification of sugar beet diseases using smartphones L Hallau, M Neumann, B Klatt, B Kleinhenz, T Klein, C Kuhn, M Röhrig, ... Plant pathology 67 (2), 399-410, 2018 | 61 | 2018 |
| Semantic and geometric reasoning for robotic grasping: a probabilistic logic approach L Antanas, P Moreno, M Neumann, RP de Figueiredo, K Kersting, ... Autonomous Robots 43, 1393-1418, 2019 | 47 | 2019 |
| Stacked Gaussian process learning M Neumann, K Kersting, Z Xu, D Schulz 2009 Ninth IEEE International Conference on Data Mining, 387-396, 2009 | 47 | 2009 |
| Graph kernels for object category prediction in task-dependent robot grasping M Neumann, P Moreno, L Antanas, R Garnett, K Kersting Online proceedings of the eleventh workshop on mining and learning with …, 2013 | 41 | 2013 |
| Explicit versus implicit graph feature maps: A computational phase transition for walk kernels N Kriege, M Neumann, K Kersting, P Mutzel 2014 IEEE international conference on data mining, 881-886, 2014 | 34 | 2014 |
| pyGPs: a Python library for Gaussian process regression and classification. M Neumann, S Huang, DE Marthaler, K Kersting J. Mach. Learn. Res. 16 (1), 2611-2616, 2015 | 30 | 2015 |
| A unifying view of explicit and implicit feature maps of graph kernels NM Kriege, M Neumann, C Morris, K Kersting, P Mutzel Data Mining and Knowledge Discovery 33, 1505-1547, 2019 | 25 | 2019 |
| Markov logic sets: Towards lifted information retrieval using pagerank and label propagation M Neumann, B Ahmadi, K Kersting Proceedings of the AAAI Conference on Artificial Intelligence 25 (1), 447-452, 2011 | 16 | 2011 |
| Capturing student feedback and emotions in large computing courses: A sentiment analysis approach M Neumann, R Linzmayer Proceedings of the 52nd ACM Technical Symposium on Computer Science …, 2021 | 14 | 2021 |
| A unifying view of explicit and implicit feature maps for structured data: systematic studies of graph kernels NM Kriege, M Neumann, C Morris, K Kersting, P Mutzel arXiv preprint arXiv:1703.00676, 2017 | 12 | 2017 |
| High-level reasoning and low-level learning for grasping: A probabilistic logic pipeline L Antanas, P Moreno, M Neumann, RP de Figueiredo, K Kersting, ... arXiv preprint arXiv:1411.1108, 2014 | 9 | 2014 |
| Markov logic mixtures of Gaussian processes: Towards machines reading regression data M Schiegg, M Neumann, K Kersting Artificial Intelligence and Statistics, 1002-1011, 2012 | 9 | 2012 |
| Propagation kernels for partially labeled graphs M Neumann, R Garnett, P Moreno, N Patricia, K Kersting ICML–2012 Workshop on Mining and Learning with Graphs (MLG–2012), Edinburgh, UK, 2012 | 9 | 2012 |
| AI Education Matters: A First Introduction to Modeling and Learning using the Data Science Workflow M Neumann AI Matters 5 (3), 21-24, 2019 | 7 | 2019 |
Kristian KerstingProfessor of AI & ML, TU Darmstadt, Co-Director hessian.ai, DFKI, Germany, CLAIRE & ELLISVerified email at cs.tu-darmstadt.de
