A Brief Tour of Deep Learning from a Statistical Perspective

被引：2

作者：

Nalisnick, Eric ^{[1
]}

Smyth, Padhraic ^{[2
,3
]}

Tran, Dustin ^{[4
]}

机构：

[1] Univ Amsterdam, Informat Inst, Amsterdam, Netherlands

[2] Univ Calif Irvine, Dept Comp Sci, Irvine, CA USA

[3] Univ Calif Irvine, Dept Stat, Irvine, CA USA

[4] Google Brain, Mountain View, CA USA

来源：

ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION | 2023年 / 10卷

基金：

美国国家科学基金会;

关键词：

deep learning; neural networks; pattern recognition; optimization; NEURAL-NETWORKS; REPRESENTATIONS; CLASSIFIERS; PREDICTION; INFERENCE; MODELS; SYSTEM;

D O I：

10.1146/annurev-statistics-032921-013738

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

We expose the statistical foundations of deep learning with the goal of facilitating conversation between the deep learning and statistics communities. We highlight core themes at the intersection; summarize key neural models, such as feedforward neural networks, sequential neural networks, and neural latent variable models; and link these ideas to their roots in probability and statistics.We also highlight research directions in deep learning where there are opportunities for statistical contributions.

引用

页码：219 / 246

页数：28

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