Reproducibility for Deep Learning
(چکیده مقاله) :
Abstract :
Deep learning, a set of approaches using artificial neural networks, has generated rapid recent advancements
in machine learning. Deep learning does, however, have the potential to reduce the reproducibility
of scientific results. Model outputs are critically dependent on the data and processing approach used to
initially generate the model, but this provenance information is usually lost during model training. To avoid
a future reproducibility crisis, we need to improve our deep-learning model management. The FAIR principles
for data stewardship and software/workflow implementation give excellent high-level guidance on ensuring
effective reuse of data and software. We suggest some specific guidelines for the generation and use of
deep-learning models in science and explain how these relate to the FAIR principles. We then present
dtoolAI, a Python package that we have developed to implement these guidelines. The package implements
automatic capture of provenance information during model training and simplifies model distribution.
in machine learning. Deep learning does, however, have the potential to reduce the reproducibility
of scientific results. Model outputs are critically dependent on the data and processing approach used to
initially generate the model, but this provenance information is usually lost during model training. To avoid
a future reproducibility crisis, we need to improve our deep-learning model management. The FAIR principles
for data stewardship and software/workflow implementation give excellent high-level guidance on ensuring
effective reuse of data and software. We suggest some specific guidelines for the generation and use of
deep-learning models in science and explain how these relate to the FAIR principles. We then present
dtoolAI, a Python package that we have developed to implement these guidelines. The package implements
automatic capture of provenance information during model training and simplifies model distribution.
(توضیحات تکمیلی) :
(توضیحات تکمیلی) :
Description :
مقاله ISI انگلیسی اصلی
سال انتشار: 2020
فایل ISI انگلیسی اصلی ، با فرمت Pdf
تعداد صفحات فایل ISI انگلیسی اصلی: 11 صفحه
سال انتشار: 2020
فایل ISI انگلیسی اصلی ، با فرمت Pdf
تعداد صفحات فایل ISI انگلیسی اصلی: 11 صفحه
Authors / Descriptions(نویسندگان/توضیحات): سال انتشار 2020 \ مقاله ISI انگلیسی اصلی \ نویسندگان: Matthew Hartley1,2,* and Tjelvar S.G. Olsson1
Sent date(تاریخ ارسال) :
1399/06/14 | 9/4/2020
Number of visits(تعداد بازدید):
584
Key words (کلمات کلیدی):
machine learning , Reproducibility , Deep learning , teaching computers
Number of pages(تعداد صفحات) :
11
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