92 citations · 191 across the 7 of their papers we have counts for
7 papers
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…
Engaging with Researchers and Raising Awareness of FAIR and Open Science through the FAIR+ Implementation Survey Tool (FAIRIST)
Christine R. Kirkpatrick, Kevin L. Coakley, Julie Christopher +1
Six years after the seminal paper on FAIR was published, researchers still struggle to understand how to implement FAIR. For many researchers FAIR promises long-term benefits for n…
FAIR for AI: An interdisciplinary and international community building perspective
E. A. Huerta, Ben Blaiszik, L. Catherine Brinson +21
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with th…
Playing catch-up in building an open research commons
Philip E. Bourne, Vivien Bonazzi, Amy Brand +12
On August 2, 2021 a group of concerned scientists and US funding agency and federal government officials met for an informal discussion to explore the value and need for a well-coo…
DataPerf: Benchmarks for Data-Centric AI Development
Mark Mazumder, Colby Banbury, Xiaozhe Yao +42
Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithf…
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…
Bioinformatics Computational Cluster Batch Task Profiling with Machine Learning for Failure Prediction
Christopher Harrison, Christine R. Kirkpatrick, Inês Dutra
Motivation: Traditional computational cluster schedulers are based on user inputs and run time needs request for memory and CPU, not IO. Heavily IO bound task run times, like ones…