线性系统的非线性计算

IF 17.6 1区物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY

Nature Physics Pub Date : 2024-07-09 DOI:10.1038/s41567-024-02531-y

Peter L. McMahon

引用次数: 0

摘要

非线性对于机器学习中的复杂任务至关重要，但通常很难在电子领域之外进行工程设计。通过将输入编码为系统参数，线性系统可以实现高效的可训练非线性计算。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Nonlinear computation with linear systems

Nonlinearity is crucial for sophisticated tasks in machine learning but is often difficult to engineer outside of electronics. By encoding the inputs in parameters of the system, linear systems can realize efficiently trainable nonlinear computations.

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来源期刊

Nature Physics 物理-物理：综合

CiteScore

30.40

自引率

2.00%

发文量

349

审稿时长

4-8 weeks

期刊介绍： Nature Physics is dedicated to publishing top-tier original research in physics with a fair and rigorous review process. It provides high visibility and access to a broad readership, maintaining high standards in copy editing and production, ensuring rapid publication, and maintaining independence from academic societies and other vested interests. The journal presents two main research paper formats: Letters and Articles. Alongside primary research, Nature Physics serves as a central source for valuable information within the physics community through Review Articles, News & Views, Research Highlights covering crucial developments across the physics literature, Commentaries, Book Reviews, and Correspondence.