2 citations · 2 across the 3 of their papers we have counts for
3 papers
ReproScore: Separating Readiness from Outcome in Research Software Reproducibility Assessment
Sheeba Samuel, Daniel Mietchen, Jungsan Kim +2
Digital libraries curate millions of research software artefacts yet lack scalable infrastructure for assessing whether those artefacts remain executable. Existing automated assess…
Learning to be Reproducible: Custom Loss Design for Robust Neural Networks
Waqas Ahmed, Sheeba Samuel, Kevin Coakley +2
To enhance the reproducibility and reliability of deep learning models, we address a critical gap in current training methodologies: the lack of mechanisms that ensure consistent a…
Evaluating the method reproducibility of deep learning models in the biodiversity domain
Waqas Ahmed, Vamsi Krishna Kommineni, Birgitta König-Ries +3
Artificial Intelligence (AI) is revolutionizing biodiversity research by enabling advanced data analysis, species identification, and habitats monitoring, thereby enhancing conserv…