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- W4387005238 abstract "Machine learning has made significant advancements in precision medicine, resulting in the development of various deep learning applications. For instance, in cancer drug response prediction, numerous deep learning models have been created. However, comparing these models across vast configurations of hyperparameters and data sets can be challenging. In this paper, we introduce a new scalable workflow suite that aims to answer questions that arise when comparing different models developed by different teams on similar or the same problems. We explain the problem in more detail and discuss our approach using near-exascale or exascale computers." @default.
- W4387005238 created "2023-09-26" @default.
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- W4387005238 date "2023-10-09" @default.
- W4387005238 modified "2023-09-26" @default.
- W4387005238 title "An Automation Framework for Comparison of Cancer Response Models Across Configurations" @default.
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- W4387005238 doi "https://doi.org/10.1109/e-science58273.2023.10254830" @default.
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