Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Jun 24, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K

稳定张量神经网络,以实现高效的深度学习.

Elizabeth Newman1, Lior Horesh2, Haim Avron3

  • 1Department of Mathematics, Emory University, Atlanta, GA, United States.

Frontiers in big data
|June 14, 2024
PubMed
概括
此摘要是机器生成的。

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

The need for verification in artificial intelligence-driven scientific discovery.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same author

Real-World Safety Data From the World Apheresis Association Registry for the Spectra Optia Apheresis System.

Journal of clinical apheresis·2025
Same author

The number of apheresis procedures to treat immune-mediated neurological diseases is on the rise.

Transfusion and apheresis science : official journal of the World Apheresis Association : official journal of the European Society for Haemapheresis·2025
Same author

Update of data from the world apheresis association (WAA) registry.

Transfusion and apheresis science : official journal of the World Apheresis Association : official journal of the European Society for Haemapheresis·2025
Same author

Evolving scientific discovery by unifying data and background knowledge with AI Hilbert.

Nature communications·2024
Same author

The world apheresis association registry, 2023 update.

Transfusion and apheresis science : official journal of the World Apheresis Association : official journal of the European Society for Haemapheresis·2023
Same journal

Deep learning model to predict COPD hospital admissions based on meteorological data: a medical meteorological forecast.

Frontiers in big data·2026
Same journal

Where diverse populations gather: transit accessibility and the spatial structure of social mixing.

Frontiers in big data·2026
Same journal

Inner layer security reinforcement for instant payment systems: a dual layer encryption-steganography evaluation in Brunei's digital payment context.

Frontiers in big data·2026
Same journal

Measuring the impact of virtualization and containerization on the environment when using GPUs for processing the AI models.

Frontiers in big data·2026
Same journal

Using artificial intelligence to improve governance and public services in Africa.

Frontiers in big data·2026
Same journal

Case count metric for comparative analysis of entity resolution results.

Frontiers in big data·2026
查看所有相关文章

张量神经网络 (t-NN) 为复杂的数据提供了高效的深度学习. 这些网络利用张量表示来减少参数,提高速度和内存,用于高维函数近似.

科学领域:

  • 计算数学 计算数学 计算数学
  • 深度学习 (Deep Learning) 是一种深度学习.
  • 张量计计算 张量计计算

背景情况:

  • 深度神经网络 (DNN) 是有效的高维函数近似器.
  • 张量在深度学习中自然会作为数据,权重和特征出现,通常会导致计算瓶.
  • 有效的参数化对于训练DNN处理复杂的多维数据至关重要.

研究的目的:

  • 为高效的DNN参数化提出张量神经网络 (t-NN).
  • 为了利用张量表示和处理用于高维数据学习.
  • 将现有的DNN框架,如稳定的神经网络,扩展到一个多维张量语境.

主要方法:

  • 开发了张量神经网络 (t-NN) 作为完全连接网络的延伸.
  • 利用矩阵模拟式张量-张量产物来保持代数性质并捕获相关性.
  • 扩展了稳定神经网络的框架,将DNN解释为微分方程离散.

主要成果:

  • t-NN可以在一个缩小,强大的参数空间中进行高效的训练.
  • 在减小维度 (自动编码器) 和分类任务中,t-NN的参数优势已被证明.
  • 在使用完全连接和稳定的t-NN变体的MNIST和CIFAR-10基准成像数据集上进行实证验证.
关键词:
深度学习是一种深度学习.图像的分类图像的分类.反向问题是反向的问题.机器学习是机器学习.张量代数的张量代数.

更多相关视频

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

517

相关实验视频

Last Updated: Jun 24, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

517

结论:

  • t-NN 提供了一种高效,强大的深度学习方法,使用高维,多路数据进行深度学习.
  • 提出的基于张数的方法解决了DNN中的计算瓶.
  • t-NNs为推进深度学习架构和应用提供了一个有前途的方向.