Second-Order Networks in PyTorch

被引：2

作者：

Brooks, Daniel ^{[1
,2
]}

Schwander, Olivier ^{[2
]}

Barbaresco, Frederic ^{[1
]}

Schneider, Jean-Yves ^{[1
]}

Cord, Matthieu ^{[2
]}

机构：

[1] Thales Land & Air Syst, Adv Radar Concepts, Limours, France

[2] Sorbonne Univ, LIP6, CNRS, Lab Informat Paris 6, F-75005 Paris, France

来源：

GEOMETRIC SCIENCE OF INFORMATION | 2019年 / 11712卷

关键词：

SPD matrix; Covariance; Second-order neural network; Riemannian machine learning;

D O I：

10.1007/978-3-030-26980-7_78

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Classification of Symmetric Positive Definite (SPD) matrices is gaining momentum in a variety machine learning application fields. In this work we propose a Python library which implements neural networks on SPD matrices, based on the popular deep learning framework Pytorch.

引用

页码：751 / 758

页数：8

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