92 citations · 147 across the 10 of their papers we have counts for
10 papers
The Shift Toward Open and Reproducible AI Research
Kevin L Coakley, Thijs Snelleman, Holger Hoos +1
The reproducibility crisis has directed the AI research community toward improving documentation practices. Several studies have identified methodological issues, and in response,…
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…
Automated Reproducibility Has a Problem Statement Problem
Thijs Snelleman, Peter Lundestad Lawrence, Holger H. Hoos +1
Background. Reproducibility is essential to the scientific method, but reproduction is often a laborious task. Recent works have attempted to automate this process and relieve rese…
Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems
Aleksandra Jekic, Afroditi Natsaridou, Signe Riemer-Sørensen +2
Approximating solutions to partial differential equations (PDEs) is fundamental for the modeling of dynamical systems in science and engineering. Physics-informed neural networks (…
The Unreasonable Effectiveness of Open Science in AI: A Replication Study
Odd Erik Gundersen, Odd Cappelen, Martin Mølnå +1
A reproducibility crisis has been reported in science, but the extent to which it affects AI research is not yet fully understood. Therefore, we performed a systematic replication…
EXPRTS: Exploring and Probing the Robustness of Time Series Forecasting Models
Håkon Hanisch Kjærnli, Lluis Mas-Ribas, Hans Jakob Håland +4
When deploying time series forecasting models based on machine learning to real world settings, one often encounter situations where the data distribution drifts. Such drifts expos…
Examining the Effect of Implementation Factors on Deep Learning Reproducibility
Kevin Coakley, Christine R. Kirkpatrick, Odd Erik Gundersen
Reproducing published deep learning papers to validate their conclusions can be difficult due to sources of irreproducibility. We investigate the impact that implementation factors…
REFORMS: Reporting Standards for Machine Learning Based Science
Sayash Kapoor, Emily Cantrell, Kenny Peng +16
Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and ge…
Sources of Irreproducibility in Machine Learning: A Review
Odd Erik Gundersen, Kevin Coakley, Christine Kirkpatrick +1
Background: Many published machine learning studies are irreproducible. Issues with methodology and not properly accounting for variation introduced by the algorithm themselves or…
The Fundamental Principles of Reproducibility
Odd Erik Gundersen
Reproducibility is a confused terminology. In this paper, I take a fundamental view on reproducibility rooted in the scientific method. The scientific method is analysed and charac…