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ORCIDMarion Neumann
Person information
- affiliation: Washington University in St. Louis, MO, USA
- affiliation: Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), St. Augustin, Germany
- affiliation: University of Bonn, Germany
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2020 – today
- 2023
[j12]Michael Guerzhoy, Marion Neumann, Pat Virtue, Carolyn Jane Anderson, Yaman Kumar Singla, Alexi Orchard, Rajiv Ratn Shah:
EAAI-23 Blue Sky Ideas in Artificial Intelligence Education from the AAAI/ACM SIGAI New and Future AI Educator Program. AI Matters 9(2): 24-29 (2023)- 2022
- [j11]Michael Guerzhoy, Marion Neumann, Emmanuel Johnson, David Johnson, Henry Chai, Daniel Garijo, Zhuoyue Lyu
, Christopher J. MacLellan:
EAAI-22 Blue Sky Ideas in Artificial Intelligence Education from the AAAI/ACM SIGAI New and Future AI Educator Program. AI Matters 8(2): 16-21 (2022) - 2021
- [c11]Marion Neumann, Robin Linzmayer:
Capturing Student Feedback and Emotions in Large Computing Courses: A Sentiment Analysis Approach. SIGCSE 2021: 541-547 - 2020
- [c10]Todd W. Neller, Stephen Keeley, Michael Guerzhoy, Wolfgang Hönig, Jiaoyang Li, Sven Koenig, Ameet Soni, Krista Thomason, Lisa Zhang, Bibin Sebastian, Cinjon Resnick, Avital Oliver, Surya Bhupatiraju, Kumar Krishna Agrawal, James Allingham, Sejong Yoon, Jonathan Chen, Tom Larsen, Marion Neumann, Narges Norouzi, Ryan Hausen, Matthew Evett:
Model AI Assignments 2020. AAAI 2020: 13509-13511 - [i4]Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, Marion Neumann:
TUDataset: A collection of benchmark datasets for learning with graphs. CoRR abs/2007.08663 (2020)
2010 – 2019
- 2019
- [j10]Marion Neumann:
AI profiles: an interview with Thomas Dietterich. AI Matters 5(1): 7-9 (2019) - [j9]Marion Neumann:
AI profiles: an interview with Leslie Kaelbling. AI Matters 5(2): 4-5 (2019) - [j8]Sven Koenig, Sanmay Das, Rosemary D. Paradis, John P. Dickerson, Yolanda Gil, Katherine Guo, Benjamin Kuipers, Iolanda Leite, Hang Ma, Nicholas Mattei, Amy McGovern, Larry R. Medsker, Todd W. Neller, Marion Neumann, Plamen Petrov, Michael Rovatsos, David G. Stork:
ACM SIGAI activity report. AI Matters 5(3): 6-11 (2019) - [j7]Marion Neumann:
AI education matters: a first introduction to modeling and learning using the data science workflow. AI Matters 5(3): 21-24 (2019) - [j6]Laura Antanas, Plinio Moreno, Marion Neumann, Rui Pimentel de Figueiredo, Kristian Kersting, José Santos-Victor, Luc De Raedt:
Semantic and geometric reasoning for robotic grasping: a probabilistic logic approach. Auton. Robots 43(6): 1393-1418 (2019) - [j5]Nils M. Kriege, Marion Neumann, Christopher Morris, Kristian Kersting, Petra Mutzel:
A unifying view of explicit and implicit feature maps of graph kernels. Data Min. Knowl. Discov. 33(6): 1505-1547 (2019) - [c9]Todd W. Neller, Raja Sooriamurthi, Michael Guerzhoy, Lisa Zhang, Paul Talaga, Christopher Archibald, Adam Summerville, Joseph C. Osborn, Cinjon Resnick, Avital Oliver, Surya Bhupatiraju, Kumar Krishna Agrawal, Nate Derbinsky, Elena Strange, Marion Neumann, Jonathan Chen, Zac Christensen, Michael Wollowski, Oscar Youngquist:
Model AI Assignments 2019. AAAI 2019: 9751-9753 - 2018
- [j4]Marion Neumann:
AI profiles: an interview with Kristian Kersting. AI Matters 4(3): 19-21 (2018) - [j3]Marion Neumann:
AI profiles: an interview with Iolanda Leite. AI Matters 4(4): 7-8 (2018) - [c8]Muhan Zhang, Zhicheng Cui, Marion Neumann, Yixin Chen:
An End-to-End Deep Learning Architecture for Graph Classification. AAAI 2018: 4438-4445 - 2017
- [i3]Nils M. Kriege, Marion Neumann, Christopher Morris, Kristian Kersting, Petra Mutzel:
A Unifying View of Explicit and Implicit Feature Maps for Structured Data: Systematic Studies of Graph Kernels. CoRR abs/1703.00676 (2017) - 2016
- [j2]Marion Neumann, Roman Garnett, Christian Bauckhage, Kristian Kersting:
Propagation kernels: efficient graph kernels from propagated information. Mach. Learn. 102(2): 209-245 (2016) - 2015
- [b1]Marion Neumann:
Learning with Graphs using Kernels from Propagated Information. University of Bonn, 2015 - [j1]Marion Neumann, Shan Huang, Daniel E. Marthaler, Kristian Kersting:
pyGPs: a Python library for Gaussian process regression and classification. J. Mach. Learn. Res. 16: 2611-2616 (2015) - 2014
- [c7]Nils M. Kriege, Marion Neumann, Kristian Kersting, Petra Mutzel:
Explicit Versus Implicit Graph Feature Maps: A Computational Phase Transition for Walk Kernels. ICDM 2014: 881-886 - [c6]Marion Neumann, Lisa Hallau, Benjamin Klatt, Kristian Kersting, Christian Bauckhage:
Erosion Band Features for Cell Phone Image Based Plant Disease Classification. ICPR 2014: 3315-3320 - [i2]Marion Neumann, Roman Garnett, Christian Bauckhage, Kristian Kersting:
Propagation Kernels. CoRR abs/1410.3314 (2014) - [i1]Laura Antanas, Plinio Moreno, Marion Neumann, Rui Pimentel de Figueiredo, Kristian Kersting, José Santos-Victor, Luc De Raedt:
High-level Reasoning and Low-level Learning for Grasping: A Probabilistic Logic Pipeline. CoRR abs/1411.1108 (2014) - 2013
- [c5]Marion Neumann, Roman Garnett, Kristian Kersting:
Coinciding Walk Kernels: Parallel Absorbing Random Walks for Learning with Graphs and Few Labels. ACML 2013: 357-372 - 2012
- [c4]Marion Neumann, Novi Patricia, Roman Garnett, Kristian Kersting:
Efficient Graph Kernels by Randomization. ECML/PKDD (1) 2012: 378-393 - [c3]Martin Schiegg, Marion Neumann, Kristian Kersting:
Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data. AISTATS 2012: 1002-1011 - 2011
- [c2]Marion Neumann, Babak Ahmadi, Kristian Kersting:
Markov Logic Sets: Towards Lifted Information Retrieval Using PageRank and Label Propagation. AAAI 2011: 447-452
2000 – 2009
- 2009
- [c1]Marion Neumann, Kristian Kersting, Zhao Xu, Daniel Schulz:
Stacked Gaussian Process Learning. ICDM 2009: 387-396
Coauthor Index
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