Marion Neumann
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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
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
Benchmark data sets for graph kernels
K Kersting, NM Kriege, C Morris, P Mutzel, M Neumann
Propagation kernels: efficient graph kernels from propagated information
M Neumann, R Garnett, C Bauckhage, K Kersting
Machine learning 102, 209-245, 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
Efficient graph kernels by randomization
M Neumann, N Patricia, R Garnett, K Kersting
Machine Learning and Knowledge Discovery in Databases: European Conference …, 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
Stacked Gaussian process learning
M Neumann, K Kersting, Z Xu, D Schulz
2009 Ninth IEEE International Conference on Data Mining, 387-396, 2009
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
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
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
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
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
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
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
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
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
Markov logic mixtures of Gaussian processes: Towards machines reading regression data
M Schiegg, M Neumann, K Kersting
Artificial Intelligence and Statistics, 1002-1011, 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
AI Education Matters: A First Introduction to Modeling and Learning using the Data Science Workflow
M Neumann
AI Matters 5 (3), 21-24, 2019
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