受强制性开放获取政策约束的文章 - Marion Neumann了解详情
可在其他位置公开访问的文章：15 篇
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
强制性开放获取政策： US National Institutes of Health
Propagation kernels: efficient graph kernels from propagated information
M Neumann, R Garnett, C Bauckhage, K Kersting
Machine learning 102, 209-245, 2016
强制性开放获取政策： German Research Foundation
Efficient graph kernels by randomization
M Neumann, N Patricia, R Garnett, K Kersting
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2012
强制性开放获取政策： Fraunhofer-Gesellschaft
Stacked Gaussian process learning
M Neumann, K Kersting, Z Xu, D Schulz
2009 Ninth IEEE International Conference on Data Mining, 387-396, 2009
强制性开放获取政策： Fraunhofer-Gesellschaft
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
强制性开放获取政策： German Research Foundation, Fraunhofer-Gesellschaft
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
强制性开放获取政策： Fraunhofer-Gesellschaft
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
强制性开放获取政策： German Research Foundation
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
强制性开放获取政策： Fraunhofer-Gesellschaft
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
强制性开放获取政策： US National Science Foundation
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
强制性开放获取政策： German Research Foundation
Markov logic mixtures of Gaussian processes: Towards machines reading regression data
M Schiegg, M Neumann, K Kersting
Artificial Intelligence and Statistics, 1002-1011, 2012
强制性开放获取政策： Fraunhofer-Gesellschaft
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
强制性开放获取政策： Fraunhofer-Gesellschaft
Coinciding walk kernels: Parallel absorbing random walks for learning with graphs and few labels
M Neumann, R Garnett, K Kersting
Asian conference on machine learning, 357-372, 2013
强制性开放获取政策： German Research Foundation, Fraunhofer-Gesellschaft
EAAI-23 Blue Sky Ideas in Artificial Intelligence Education from the AAAI/ACM SIGAI New and Future AI Educator Program
M Guerzhoy, M Neumann, P Virtue, CJ Anderson, YK Singla, A Orchard, ...
AI Matters 9 (2), 24-29, 2023
强制性开放获取政策： US National Science Foundation, US Department of Defense
Coinciding Walk Kernels
M Neumann, R Garnett, K Kersting
Eleventh Workshop on Mining and Learning with Graphs (MLG-13), 2013
强制性开放获取政策： German Research Foundation, Fraunhofer-Gesellschaft
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