Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research.

TMLR Paper2366 Authors

Abstract:

Difficulties in replication and reproducibility of empirical evidences in machine learning
research have become a prominent topic in recent years. Ensuring that machine learning
research results are sound and reliable requires reproducibility, which verifies the reliability of
research findings using the same code and data. This promotes open and accessible research,
robust experimental workflows, and the rapid integration of new findings. Evaluating the
degree to which research publications support these different aspects of reproducibility is
one goal of the present work. For this we introduce an ontology of reproducibility in machine
learning and apply it to methods for graph neural networks.
Building on these efforts we turn towards another critical challenge in machine learning,
namely the curse of dimensionality, which poses challenges in data collection, representation,
and analysis, making it harder to find representative data and impeding the training and
inference processes. Using the closely linked concept of geometric intrinsic dimension we
investigate to which extend the used machine learning models are influenced by the intrinsic
dimension of the data sets they are trained on.

Submission Length: Long submission (more than 12 pages of main content)

Assigned Action Editor: ~Roberto_Imbuzeiro_Oliveira1

Submission Number: 2366