目录 概 主要内容 定理1 定理2 定理3 定理4 定理1的证明 Lu Z, Pu H, Wang F, et al. The expressive power of neural networks: a view from the width[C]. neural information processing systems, 2017: 6232-6240. @article{lu2017the, title={The expressive power of neural networks:…

CNN综述文章 的翻译 [2019 CVPR] A Survey of the Recent Architectures of Deep Convolutional Neural Networks 翻译 综述深度卷积神经网络架构:从基本组件到结构创新 目录 摘要    1.引言    2.CNN基本组件        2.1 卷积层        2.2 池化层        2.3 激活函数        2.4 批次归一化        2.5 Dropout        2.6 全连接层…

1.What does the analogy “AI is the new electricity” refer to?  (B) A. Through the “smart grid”, AI is delivering a new wave of electricity. B. Similar to electricity starting about 100 years ago, AI is transforming multiple industries. C. AI is power…

1. 摘要 卷积和循环神经网络中的操作都是一次处理一个局部邻域,在这篇文章中,作者提出了一个非局部的操作来作为捕获远程依赖的通用模块. 受计算机视觉中经典的非局部均值方法启发,我们的非局部操作计算某一位置的响应为所有位置特征的加权和.而且,这个模块可以插入到许多计算机视觉网络架构中去. 2. 介绍 在深度神经网络中,捕获远程依赖非常重要.卷积神经网络依靠大的感知野来对远程依赖建模,这是通过重复叠加卷积块来实现的.但同时,它也有一些限制.首先,它在计算上效率低下.其次,它会导致需要仔细解决的优化难…

今天看到一篇关于检测的论文<SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving>,论文中的效果还不错,后来查了一下,有一个Tensorflow版本的实现,因此在自己的机器上配置了Tensorflow的环境,然后将其给出的demo跑通了,其中遇到了一些小问题,通过查找网络上的资料解决掉了,在这里…

About this Course If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "s…

When a golf player is first learning to play golf, they usually spend most of their time developing a basic swing. Only gradually do they develop other shots, learning to chip, draw and fade the ball, building on and modifying their basic swing. In a…

This past summer I interned at Flipboard in Palo Alto, California. I worked on machine learning based problems, one of which was Image Upscaling. This post will show some preliminary results, discuss our model and its possible applications to Flipboa…

Table of Contents: Architecture Overview ConvNet Layers Convolutional Layer Pooling Layer Normalization Layer Fully-Connected Layer Converting Fully-Connected Layers to Convolutional Layers ConvNet Architectures Layer Patterns Layer Sizing Patterns C…

Image Scaling using Deep Convolutional Neural Networks This past summer I interned at Flipboard in Palo Alto, California. I worked on machine learning based problems, one of which was Image Upscaling. This post will show some preliminary results, dis…