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

arXiv:2403.08438v1 Announce Type: new
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 …

abstract arxiv become code cs.ai cs.lg dimensionality graph graph neural network intrinsic investigation machine machine learning network neural network reliability replication reproducibility research results sound type