Representation Learning: A Statistical Perspective

Jianwen Xie; Ruiqi Gao; Erik Nijkamp

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Representation Learning: A Statistical Perspective

机译：代表学习：统计观点

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摘要

Learning representations of data is an important problem in statistics and machine learning. While the origin of learning representations can be traced back to factor analysis and multidimensional scaling in statistics, it has become a central theme in deep learning with important applications in computer vision and computational neuroscience. In this article, we review recent advances in learning representations from a statistical perspective. In particular, we review the following two themes: (a) unsupervised learning of vector representations and (b) learning of both vector and matrix representations.

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《Annual Review of Statistics and Its Application》 |2020年第1期|共33页
作者
Jianwen Xie; Ruiqi Gao; Erik Nijkamp;
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作者单位

Hikvision Research Institute Santa Clara California 95054 USA;

Department of Statistics University of California Los Angeles California 90095 USA;

Department of Statistics University of California Los Angeles California 90095 USA;

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原文格式 PDF
正文语种 eng
中图分类统计学;
关键词
unsupervised learning; generative representations; relative representations; predictive representations; vector representations; matrix representations;

机译：无人监督的学习;生成陈述;相对陈述;预测表示;矢量表示;矩阵表示;

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Representation Learning: A Statistical Perspective

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