Summary of Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research, by Tobias Hille and Maximilian Stubbemann and Tom Hanika
Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research
by Tobias Hille, Maximilian Stubbemann, Tom Hanika
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses two crucial issues in machine learning research: reproducibility and the curse of dimensionality. To ensure the reliability of research findings, the authors introduce an ontology for reproducing machine learning experiments, focusing on graph neural networks. The goal is to evaluate how well publications support open and accessible research, robust experimental workflows, and rapid integration of new findings. The paper also investigates the impact of data set intrinsic dimension on machine learning model performance using geometric intrinsic dimension. By understanding these challenges, researchers can develop more effective methods for training and inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study tackles two big problems in machine learning research: making sure results are reliable and dealing with too much data. The authors create a way to check if research is trustworthy by re-running the same experiment with the same code and data. This helps ensure that new discoveries can be quickly added to existing knowledge. The paper also looks at how the complexity of data affects machine learning model performance. By understanding these issues, researchers can develop better ways to analyze and use data. |
Keywords
* Artificial intelligence * Inference * Machine learning
