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Maximilian Stubbemann
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Books and Theses
- 2023
- [b1]Maximilian Stubbemann:
Orometry, intrinsic dimensionality and learning: Novel insights into network data. University of Kassel, Germany, 2023
Journal Articles
- 2023
- [j2]Maximilian Stubbemann, Tom Hanika, Friedrich Martin Schneider:
Intrinsic Dimension for Large-Scale Geometric Learning. Trans. Mach. Learn. Res. 2023 (2023) - 2021
- [j1]Tobias Koopmann, Maximilian Stubbemann, Matthias Kapa, Michael Paris, Guido Buenstorf, Tom Hanika, Andreas Hotho, Robert Jäschke, Gerd Stumme:
Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research. Scientometrics 126(12): 9847-9868 (2021)
Conference and Workshop Papers
- 2024
- [c4]Christian Klötergens, Vijaya Krishna Yalavarthi, Maximilian Stubbemann, Lars Schmidt-Thieme:
Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting. ECML/PKDD (4) 2024: 421-436 - 2023
- [c3]Maximilian Stubbemann, Gerd Stumme:
The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks. ECML/PKDD (3) 2023: 177-192 - 2022
- [c2]Maximilian Stubbemann, Gerd Stumme:
LG4AV: Combining Language Models and Graph Neural Networks for Author Verification. IDA 2022: 315-326 - 2020
- [c1]Maximilian Stubbemann, Tom Hanika, Gerd Stumme:
Orometric Methods in Bounded Metric Data. IDA 2020: 496-508
Parts in Books or Collections
- 2022
- [p1]Dominik Dürrschnabel, Tom Hanika, Maximilian Stubbemann:
FCA2VEC: Embedding Techniques for Formal Concept Analysis. Complex Data Analytics with Formal Concept Analysis 2022: 47-74
Informal and Other Publications
- 2024
- [i12]Tim Dernedde, Daniela Thyssens, Sören Dittrich, Maximilian Stubbemann, Lars Schmidt-Thieme:
Moco: A Learnable Meta Optimizer for Combinatorial Optimization. CoRR abs/2402.04915 (2024) - [i11]Kiran Madhusudhanan, Gunnar Behrens, Maximilian Stubbemann, Lars Schmidt-Thieme:
ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing. CoRR abs/2403.03812 (2024) - [i10]Tobias Hille, Maximilian Stubbemann, Tom Hanika:
Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research. CoRR abs/2403.08438 (2024) - [i9]Johannes Burchert, Thorben Werner, Vijaya Krishna Yalavarthi, Diego Coello de Portugal, Maximilian Stubbemann, Lars Schmidt-Thieme:
Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training. CoRR abs/2404.06966 (2024) - [i8]Christian Klötergens, Vijaya Krishna Yalavarthi, Maximilian Stubbemann, Lars Schmidt-Thieme:
Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting. CoRR abs/2405.03582 (2024) - [i7]Thorben Werner, Johannes Burchert, Maximilian Stubbemann, Lars Schmidt-Thieme:
A Cross-Domain Benchmark for Active Learning. CoRR abs/2408.00426 (2024) - 2023
- [i6]Maximilian Stubbemann, Tobias Hille, Tom Hanika:
Selecting Features by their Resilience to the Curse of Dimensionality. CoRR abs/2304.02455 (2023) - 2022
- [i5]Maximilian Stubbemann, Tom Hanika, Friedrich Martin Schneider:
Intrinsic Dimension for Large-Scale Geometric Learning. CoRR abs/2210.05301 (2022) - 2021
- [i4]Maximilian Stubbemann, Gerd Stumme:
LG4AV: Combining Language Models and Graph Neural Networks for Author Verification. CoRR abs/2109.01479 (2021) - [i3]Maximilian Stubbemann, Gerd Stumme:
The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks. CoRR abs/2110.13774 (2021) - 2019
- [i2]Maximilian Stubbemann, Tom Hanika, Gerd Stumme:
Orometric Methods in Bounded Metric Data. CoRR abs/1907.09239 (2019) - [i1]Dominik Dürrschnabel, Tom Hanika, Maximilian Stubbemann:
FCA2VEC: Embedding Techniques for Formal Concept Analysis. CoRR abs/1911.11496 (2019)
Coauthor Index
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