通过内部高维混沌活动生成建模。

IF 2.4 3区 物理与天体物理 Q1 Mathematics
Samantha J Fournier, Pierfrancesco Urbani
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引用次数: 0

摘要

生成建模的目的是产生新的数据点,其统计属性与训练数据集中的数据点相似。近年来,出现了大量的机器学习技术和设置,可以以出色的性能实现这一目标。在大多数这些设置中,人们将训练数据集与噪声结合使用,噪声被添加为统计变异性的来源,对于生成任务至关重要。在这里,我们探索了在高维混沌系统中使用内部混沌动力学作为从训练数据集生成新数据点的方法的想法。我们表明,简单的学习规则可以在一组香草架构中实现这一目标,并通过标准精度度量来表征生成的数据点的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generative modeling through internal high-dimensional chaotic activity.

Generative modeling aims to produce new data points whose statistical properties resemble those in a training dataset. In recent years, there has been a burst of machine learning techniques and settings that can achieve this goal with remarkable performances. In most of these settings, one uses the training dataset in conjunction with noise, which is added as a source of statistical variability and is essential for the generative task. Here, we explore the idea of using internal chaotic dynamics in high-dimensional chaotic systems as a way to generate new data points from a training dataset. We show that simple learning rules can achieve this goal within a set of vanilla architectures and characterize the quality of the generated data points through standard accuracy measures.

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来源期刊
Physical review. E
Physical review. E 物理-物理:流体与等离子体
CiteScore
4.60
自引率
16.70%
发文量
0
审稿时长
3.3 months
期刊介绍: Physical Review E (PRE), broad and interdisciplinary in scope, focuses on collective phenomena of many-body systems, with statistical physics and nonlinear dynamics as the central themes of the journal. Physical Review E publishes recent developments in biological and soft matter physics including granular materials, colloids, complex fluids, liquid crystals, and polymers. The journal covers fluid dynamics and plasma physics and includes sections on computational and interdisciplinary physics, for example, complex networks.
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