Learning both weights and connections for efficient neural networks

摘要 神经网络既是计算密集型又是内存密集型,使得它们难以在嵌入式系统上部署.此外,传统网络在训练开始之前固定架构;因此,训练无法改善架构.为了解决这些限制,我们描述了一种方法,通过仅学习重要连接,将神经网络所需的存储和计算减少一个数量级而不影响其准确性.我们的方法使用三步法修剪冗余连接.首先,我们训练网络以了解哪些连接很重要.接下来,我们修剪不重要的连接.最后,我们重新训练网络以微调剩余连接的权重.在ImageNet数据集上,我们的方法将AlexNet的参数数量从6100万减少到670万,而不会

Learning to Compare Image Patches via Convolutional Neural Networks ---  Reading Summary 2017.03.08 Target: this paper attempt to learn a geneal similarity function for comparing image patches from image data directly. There are several ways in which

话说上一次写这个笔记是13年的事情了···那时候忙着实习,找工作,毕业什么的就没写下去了,现在工作了有半年时间也算稳定了,我会继续把这个笔记写完.其实很多章节都看了,不过还没写出来,先从第5章开始吧,第2-4章比较基础,以后再补! 第5章 Neural Networks 在第3章和第4章,我们已经学过线性的回归和分类模型,这些模型由固定的基函数(basis functions)的线性组合组成.这样的模型具有有用的解析和计算特性,但是因为维度灾难(the curse of dimensionali

这学期一直在跟进 Coursera上的 Machina Learning 公开课, 老师Andrew Ng是coursera的创始人之一,Machine Learning方面的大牛.这门课程对想要了解和初步掌握机器学习的人来说是不二的选择.这门课程涵盖了机器学习的一些基本概念和方法,同时这门课程的编程作业对于掌握这些概念和方法起到了巨大的作用. 课程地址 https://www.coursera.org/learn/machine-learning 笔记主要是简要记录下课程内容,以及MATLAB

Week 4 Quiz - Key concepts on Deep Neural Networks(第四周 测验 – 深层神经网络) \1. What is the "cache" used for in our implementation of forward propagation and backward propagation?(在实现前向传播和反向传播中使用的"cache"是什么?) [ ]It is used to cache the interme

博客地址:http://www.cnblogs.com/daniel-D/p/5602254.html 新浪微博:http://weibo.com/u/2786597434 欢迎多多交流~ Main Idea 这篇论文的工作是讲 RNN 应用到推荐系统中,想法在于把一个 session 点击一系列 item 的行为看做一个序列,用来训练一个 RNN 模型.在预测阶段,把 session 已知的点击序列作为输入,用 softmax 预测该session下一个最有可能点击的item.论文想法虽然很朴

Imagine you're an engineer who has been asked to design a computer from scratch. One day you're working away in your office, designing logical circuits, setting out AND gates, OR gates, and so on, when your boss walks in with bad news. The customer h

On Explainability of Deep Neural Networks « Learning F# Functional Data Structures and Algorithms is Out! On Explainability of Deep Neural Networks During a discussion yesterday with software architect extraordinaire David Lazar regarding how everyth

NEURAL NETWORKS, PART 1: BACKGROUND Artificial neural networks (NN for short) are practical, elegant, and mathematically fascinating models for machine learning. They are inspired by the central nervous systems of humans and animals – smaller process