Výsledky vyhledávání - "Kirkpatrick, Christine"

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Examining the Effect of Implementation Factors on Deep Learning Reproducibility

Autor: Coakley, Kevin, Kirkpatrick, Christine R., Gundersen, Odd Erik

Reproducing published deep learning papers to validate their conclusions can be difficult due to sources of irreproducibility. We investigate the impact that implementation factors have on the results and how they affect reproducibility of deep learn

Externí odkaz: http://arxiv.org/abs/2312.06633

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Engaging with Researchers and Raising Awareness of FAIR and Open Science through the FAIR+ Implementation Survey Tool (FAIRIST)

Autor: Kirkpatrick, Christine R., Coakley, Kevin L., Christopher, Julie, Dutra, Ines

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 near-term effort, requires skills not yet acquired, and is one more thi

Externí odkaz: http://arxiv.org/abs/2301.10236

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FAIR for AI: An interdisciplinary and international community building perspective

Publikováno v: Scientific Data 10, 487 (2023)

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles w

Externí odkaz: http://arxiv.org/abs/2210.08973

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DataPerf: Benchmarks for Data-Centric AI Development

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 faithfulness of the underlying problems. Neglecting the fundamental importan

Externí odkaz: http://arxiv.org/abs/2207.10062

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Playing catch-up in building an open research commons

Autor: Bourne, Philip E., Bonazzi, Vivien, Brand, Amy, Carroll, Bonnie, Foster, Ian, Guha, Ramanathan V., Hanisch, Robert, Keller, Sallie Ann, Kennedy, Mary Lee, Kirkpatrick, Christine, Mons, Barend, Nusser, Sarah M., Stebbins, Michael, Strawn, George, Szalay, Alex

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-coordinated US Open Research Commons (ORC); an interoperable collection o

Externí odkaz: http://arxiv.org/abs/2208.04682

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Sources of Irreproducibility in Machine Learning: A Review

Autor: Gundersen, Odd Erik, Coakley, Kevin, Kirkpatrick, Christine, Gil, Yolanda

Background: Many published machine learning studies are irreproducible. Issues with methodology and not properly accounting for variation introduced by the algorithm themselves or their implementations are attributed as the main contributors to the i

Externí odkaz: http://arxiv.org/abs/2204.07610

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Bioinformatics Computational Cluster Batch Task Profiling with Machine Learning for Failure Prediction

Autor: Harrison, Christopher, Kirkpatrick, Christine R., Dutra, Inês

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 seen in many big data and bioinformatics problems, are dependent on th

Externí odkaz: http://arxiv.org/abs/1812.09537

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