迭代归一化流的相空间均匀数据选择

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
M. Hassanaly, Bruce A. Perry, M. Mueller, S. Yellapantula
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引用次数: 2

摘要

摘要计算和实验能力的提高正在迅速增加日常生成的科学数据量。在受内存和计算强度限制的应用程序中,过大的数据集可能会阻碍科学发现,使数据缩减成为数据驱动方法的关键组成部分。数据集正朝着两个方向增长:数据点的数量及其维度。降维通常旨在描述低维空间上的每个数据样本,而这里的重点是减少数据点的数量。提出了一种策略来选择数据点,使得它们均匀地跨越数据的相位空间。所提出的算法依赖于估计数据的概率图,并使用它来构建接受概率。当仅使用数据集的一个子集来构建概率图时,使用迭代方法来准确估计罕见数据点的概率。不是对相位空间进行装箱来估计概率图,而是用归一化流来近似其函数形式。因此,该方法自然扩展到高维数据集。所提出的框架被证明是一种可行的途径,可以在有大量数据可用的情况下实现数据高效的机器学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uniform-in-phase-space data selection with iterative normalizing flows
Abstract Improvements in computational and experimental capabilities are rapidly increasing the amount of scientific data that are 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 dimension reduction typically aims at describing each data sample on lower-dimensional space, 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 are available.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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