In this paper, we propose population matching discrepancy (PMD) for estimating the distribution distance based on samples, as well as an algorithm to learn the ...
A differentiable estimation of the distance between two distributions based on samples is important for many deep learning tasks.
This paper proposes population matching discrepancy (PMD) for estimating the distribution distance based on samples, as well as an algorithm to learn the ...
The paper defines Population Matching Discrepancy between two distributions as the Wasserstein distance between two minibatches from the distributions.
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Nov 23, 2020 · The results of this study showed that deep learning (DL)-based dimensional reduction combined with ML clustering methods is a useful tool to determine ...
Nov 20, 2023 · We propose AutoComplete, a deep learning-based imputation method to impute or 'fill-in' missing phenotypes in population-scale biobank datasets.
We describe three basic criteria (Appropriateness, Fairness, and Bias) that can be used to help evaluate the use of machine learning and AI systems.
In this paper we describe the development of two Long-Short Term Memory network architectures for small area populations.
Sep 1, 2022 · DP reduces accuracy disparity among demographic subgroups. We use relative disparity (defined in Eq. 1) as a metric to quantify performance gaps ...
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