Uniform-in-Phase-Space Data Selection with Iterative Normalizing Flows
- URL: http://arxiv.org/abs/2112.15446v1
- Date: Tue, 28 Dec 2021 20:06:28 GMT
- Title: Uniform-in-Phase-Space Data Selection with Iterative Normalizing Flows
- Authors: Malik Hassanaly and Bruce A. Perry and Michael E. Mueller and Shashank
Yellapantula
- Abstract summary: A strategy is proposed to select data points such that they uniformly span the phase-space of the data.
An iterative method is used to accurately estimate the probability of the rare data points when only a small subset of the dataset is used to construct the probability map.
The proposed framework is demonstrated as a viable pathway to enable data-efficient machine learning when abundant data is available.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Improvements in computational and experimental capabilities are rapidly
increasing the amount of scientific data that is routinely generated. In
applications that are constrained by memory and computational intensity,
excessively large datasets may hinder scientific discovery, making data
reduction a critical component of data-driven methods. Datasets are growing in
two directions: the number of data points and their dimensionality. Whereas
data compression techniques are concerned with reducing dimensionality, the
focus here is on reducing the number of data points. A strategy is proposed to
select data points such that they uniformly span the phase-space of the data.
The algorithm proposed relies on estimating the probability map of the data and
using it to construct an acceptance probability. An iterative method is used to
accurately estimate the probability of the rare data points when only a small
subset of the dataset is used to construct the probability map. Instead of
binning the phase-space to estimate the probability map, its functional form is
approximated with a normalizing flow. Therefore, the method naturally extends
to high-dimensional datasets. The proposed framework is demonstrated as a
viable pathway to enable data-efficient machine learning when abundant data is
available. An implementation of the method is available in a companion
repository (https://github.com/NREL/Phase-space-sampling).
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