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检索条件"任意字段=2019 Reproducibility in Machine Learning, RML@ICLR 2019 Workshop"
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rml@iclr 2019 workshop - reproducibility in machine learning
RML@ICLR 2019 Workshop - Reproducibility in Machine Learning
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2019 reproducibility in machine learning, rml@iclr 2019 workshop
The proceedings contain 8 papers. The topics discussed include: reproducibility and stability analysis in metric-based few-shot learning;reproducing meta-learning with differentiable closed-form solvers;challenging co...
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reproducibility and stability analysis in metric-based few-shot learning
Reproducibility and stability analysis in metric-based few-s...
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2019 reproducibility in machine learning, rml@iclr 2019 workshop
作者： Boquet, Thomas Delisle, Laure Kochetkov, Denis Schucher, Nathan Oreshkin, Boris N. Cornebise, Julien
We propose a study of the stability of several few-shot learning algorithms subject to variations in the hyper-parameters and optimization schemes while controlling the random seed. We propose a methodology for testin... 详细信息
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reproducibility in machine learning for health
Reproducibility in machine learning for health
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2019 reproducibility in machine learning, rml@iclr 2019 workshop
作者： McDermott, Matthew B.A. Wang, Shirly Marinsek, Nikki Ranganath, Rajesh Ghassemi, Marzyeh Foschini, Luca Massachusetts Institute of Technology United States University of Toronto Canada Evidation Health Inc. United States New York University United States
machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision. Th... 详细信息
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