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Found 5 papers, 1 papers with code
Probing the Robustness of Time-series Forecasting Models with CounterfacTS
Because most of the training data does not reflect such changes, the models present poor performance on the new out-of-distribution scenarios and, therefore, the impact of such events cannot be reliably anticipated ahead of time.
Examining the Effect of Implementation Factors on Deep Learning Reproducibility
To account for these implementation factors, researchers should run their experiments multiple times in different hardware and software environments to verify their conclusions are not affected.
REFORMS: Reporting Standards for Machine Learning Based Science
Machine learning (ML) methods are proliferating in scientific research.
Sources of Irreproducibility in Machine Learning: A Review
Objective: The objective of this paper is to develop a framework that enable applied data science practitioners and researchers to understand which experiment design choices can lead to false findings and how and by this help in analyzing the conclusions of reproducibility experiments.
The Fundamental Principles of Reproducibility
The scientific method is analysed and characterised in order to develop the terminology required to define reproducibility.
