【MetaPruning】2019-ICCV-MetaPruning Meta Learning for Automatic Neural Network Channel Pruning-论文阅读

MetaPruning 2019-ICCV-MetaPruning Meta Learning for Automatic Neural Network Channel Pruning Zechun Liu (HKUST).Xiangyu Zhang (MEGVII).Jian Sun(MEGVII) GitHub:251 stars Citation:20 Motivation A typical pruning approach contains three stages: training…

MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning

MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning 2019-08-11 19:48:17 Paper: https://arxiv.org/pdf/1903.10258.pdf Code: https://github.com/liuzechun/MetaPruning 1. Background and Motivation:…

[C1W2] Neural Networks and Deep Learning - Basics of Neural Network programming

第二周:神经网络的编程基础(Basics of Neural Network programming) 二分类(Binary Classification) 这周我们将学习神经网络的基础知识,其中需要注意的是,当实现一个神经网络的时候,我们需要知道一些非常重要的技术和技巧.例如有一个包含 $m$ 个样本的训练集,你很可能习惯于用一个 for 循环来遍历训练集中的每个样本,但是当实现一个神经网络的时候,我们通常不直接使用 for 循环来遍历整个训练集,所以在这周的课程中你将学会如何处理训练集.…

CheeseZH: Stanford University: Machine Learning Ex4:Training Neural Network(Backpropagation Algorithm)

1. Feedforward and cost function; 2.Regularized cost function: 3.Sigmoid gradient The gradient for the sigmoid function can be computed as: where: 4.Random initialization randInitializeWeights.m function W = randInitializeWeights(L_in, L_out) %RANDIN…

吴恩达《深度学习》-课后测验-第一门课 (Neural Networks and Deep Learning)-Week 2 - Neural Network Basics（第二周测验 - 神经网络基础）

Week 2 Quiz - Neural Network Basics(第二周测验 - 神经网络基础) 1. What does a neuron compute?(神经元节点计算什么?) [ ] A neuron computes an activation function followed by a linear function (z = Wx + b)(神经元节点先计算激活函数,再计算线性函数(z = Wx + b)) [ ] A neuron computes a linear f…

论文笔记：（2019）GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloud

目录摘要一.引言二.相关工作基于体素网格的特征学习直接从非结构化点云中学习特征从多视图模型中学习特征几何深度学习的学习特征三.GAPNet架构 3.1 GAPLayer 局部结构表示单头GAPLayer 多头机制 3.2注意力池化层 3.3 GAPNet架构四.实验 4.1分类数据集网络结构训练细节结果消融研究 4.2 语义部件分割数据集模型结构训练细节结果五.结论 GAPNet: Graph Attention based Point Neural Ne…

Discriminative Learning of Deep Convolutional Feature Point Descriptors 论文阅读笔记

介绍该文提出一种基于深度学习的特征描述方法,并且对尺度变化.图像旋转.透射变换.非刚性变形.光照变化等具有很好的鲁棒性.该算法的整体思想并不复杂,使用孪生网络从图块中提取特征信息(得到一个128维的特征向量),并且使用L2距离来描述特征之间的差异,目标是让匹配图块特征之间的距离缩短,让不匹配图块特征之间的距离增大. 数据集及模型结构数据集论文使用的是一个叫做MVS的建筑物数据集,包含了1.5M张$64 \times 64$张的灰度图来自500K个3D points. 网络的结构: 3.…

The Rise of Meta Learning

The Rise of Meta Learning 2019-10-18 06:48:37 This blog is from: https://towardsdatascience.com/the-rise-of-meta-learning-9c61ffac8564 Connor Shorten Follow Oct 16 · 9 min read https://openai.com/blog/solving-rubiks-cube/ Meta-Learning describes th…

[转载]Meta Learning单排小教学

原文链接:Meta Learning单排小教学虽然Meta Learning现在已经非常火了,但是还有很多小伙伴对于Meta Learning不是特别理解.考虑到我的这个AI游乐场将充斥着Meta Learning的分析解读及各种原创思考,所以今天Flood就和大家做一个Meta Learning小教学,希望能够用最简短,最通俗的语言来让大家明白Meta Learning的概念,Meta Learning的几种研究方法,以及Meta Learning未来的发展,带大家上分!相信这个Meta L…

论文笔记：Visual Question Answering as a Meta Learning Task

Visual Question Answering as a Meta Learning Task ECCV 2018 2018-09-13 19:58:08 Paper: http://openaccess.thecvf.com/content_ECCV_2018/papers/Damien_Teney_Visual_Question_Answering_ECCV_2018_paper.pdf 1. Introduction: 本文提出一种新的 VQA 思路,将 meta-learning 结…

深度学习课程笔记（十七）Meta-learning (Model Agnostic Meta Learning)

深度学习课程笔记(十七)Meta-learning (Model Agnostic Meta Learning) 2018-08-09 12:21:33 The video tutorial can be found from: Model Agnostic Meta Learning Related Videos: My talk for Model Agnostic Meta Learning with domain adaptation Paper: https://arxiv.org/p…

（转）Paper list of Meta Learning/ Learning to Learn/ One Shot Learning/ Lifelong Learning

Meta Learning/ Learning to Learn/ One Shot Learning/ Lifelong Learning 2018-08-03 19:16:56 本文转自:https://github.com/floodsung/Meta-Learning-Papers 1 Legacy Papers [1] Nicolas Schweighofer and Kenji Doya. Meta-learning in reinforcement learning. Neural…

论文阅读之：Deep Meta Learning for Real-Time Visual Tracking based on Target-Specific Feature Space

Deep Meta Learning for Real-Time Visual Tracking based on Target-Specific Feature Space 2018-01-04 15:58:15 写在前面:为什么要看这个paper?这篇 paper 貌似是第一个将 meta-learning 应用到 visual tracking 领域的,取得了速度和精度较好的平衡. Introduction: 我们知道,tracking 中比较重要的就是 target object…

【RS】Automatic recommendation technology for learning resources with convolutional neural network - 基于卷积神经网络的学习资源自动推荐技术

[论文标题]Automatic recommendation technology for learning resources with convolutional neural network (2016 ISET) [论文作者]Xiaoxuan Shen, Baolin Yi*, Zhaoli Zhang,Jiangbo Shu, and Hai Liu [论文链接]Paper(5-pages // Double column) <札记非FY> [摘要] 自动学习资源推荐已经成为一个越来…

2019 ICCV、CVPR、ICLR之视频预测读书笔记

2019 ICCV.CVPR.ICLR之视频预测读书笔记作者 | 文永亮学校 | 哈尔滨工业大学(深圳) 研究方向 | 视频预测.时空序列预测 ICCV 2019 CVP github地址:https://github.com/JudyYe/CVP 这是卡耐基梅隆和facebook的一篇paper,这篇论文的关键在于分解实体预测再组成,我们观察到一个场景是由不同实体经历不同运动组成的,所以这里提出的方法是通过隐式预测独立实体的未来状态,同时推理它们之间的相互作用,并使用预测状态来构成未来…

《Learning to warm up cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks》论文阅读

<Learning to warm up cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks>论文阅读 (i)问题背景: 工业界的推荐系统/广告系统现在都会用embedding技术生成物品/用户的向量.通俗点讲就是build一个向量嵌入层,把带有原始特征的输入向量转换成一个低维度的dense向量表示.推荐系统的模型一般有向量嵌入层和深度模型层两部分组成,向量嵌入层的…

什么是 Meta Learning / Learning to Learn ?

Learning to Learn Chelsea Finn Jul 18, 2017 A key aspect of intelligence is versatility – the capability of doing many different things. Current AI systems excel at mastering a single skill, such as Go, Jeopardy, or even helicopter aerobatics. But…

Machine Learning, Homework 9, Neural Nets

Machine Learning, Homework 9, Neural NetsApril 15, 2019ContentsBoston Housing with a Single Layer and R package nnet 1Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4Digit Recognition wit…

（转）Understanding, generalisation, and transfer learning in deep neural networks

Understanding, generalisation, and transfer learning in deep neural networks FEBRUARY 27, 2017 This is the first in a series of posts looking at the ‘top 100 awesome deep learning papers.’ Deviating from the normal one-paper-per-day format, I’ll ta…

Relation-Shape Convolutional Neural Network for Point Cloud Analysis(CVPR 2019)

代码:https://github.com/Yochengliu/Relation-Shape-CNN 文章:https://arxiv.org/abs/1904.07601 作者直播:https://www.bilibili.com/video/av61824733 作者维护了一个收集一系列点云论文.代码.数据集的github仓库:https://github.com/Yochengliu/awesome-point-cloud-analysis 这篇paper是CVPR 2019 Oral…

[论文阅读笔记] Adversarial Learning on Heterogeneous Information Networks

[论文阅读笔记] Adversarial Learning on Heterogeneous Information Networks 本文结构解决问题主要贡献算法原理参考文献 (1) 解决问题现有的异构网络(HIN)嵌入方法本质上可以归结为两个步骤(1)正样本生成和负样本生成(2)在这些样本上训练模型优化目标函数以得到更合适的节点嵌入.目前主流的异构网络嵌入方法存在以下几个问题: Problem 1: 首先,这些算法一般从原始网络中随机选择节点与中心节点组合生成正样本或者负样本,即,…

DAG-GNN: DAG Structure Learning with Graph Neural Networks

目录概主要内容代码 Yu Y., Chen J., Gao T. and Yu M. DAG-GNN: DAG structure learning with graph neural networks. In International Conference on Machine Learning (ICML), 2019. 概有向无环图 + GNN + VAE. 主要内容先前已经有工作(NOTEARS)讨论了如何处理线性SEM模型 \[X = A^TX + Z, \] \(A \i…

《Neural Network and Deep Learning》_chapter4

<Neural Network and Deep Learning>_chapter4: A visual proof that neural nets can compute any function文章总结(前三章翻译在百度云里) 链接:http://neuralnetworksanddeeplearning.com/chap4.html: Michael Nielsen的<Neural Network and Deep Learning>教程中的第四章主要是证明神经网络可以用…

论文笔记之：Learning Multi-Domain Convolutional Neural Networks for Visual Tracking

Learning Multi-Domain Convolutional Neural Networks for Visual Tracking CVPR 2016 本文提出了一种新的CNN 框架来处理跟踪问题.众所周知,CNN在很多视觉领域都是如鱼得水,唯独目标跟踪显得有点“慢热”,这主要是因为CNN的训练需要海量数据,纵然是在ImageNet 数据集上微调后的model 仍然不足以很好的表达要跟踪地物体,因为Tracking问题的特殊性,至于怎么特殊的,且听细细道来. 目标跟踪之所以很少被 C…

Deep Learning: Assuming a deep neural network is properly regulated, can adding more layers actually make the performance degrade?

Deep Learning: Assuming a deep neural network is properly regulated, can adding more layers actually make the performance degrade? I found this to be really puzzling. A deeper NN is supposed to be more powerful or at least equal to a shallower NN. I…

Spark MLlib Deep Learning Convolution Neural Network (深度学习-卷积神经网络)3.1

3.Spark MLlib Deep Learning Convolution Neural Network (深度学习-卷积神经网络)3.1 http://blog.csdn.net/sunbow0 Spark MLlib Deep Learning工具箱,是依据现有深度学习教程<UFLDL教程>中的算法.在SparkMLlib中的实现.详细Spark MLlib Deep Learning(深度学习)文件夹结构: 第一章Neural Net(NN) 1.源代码 2.源代码解析 3.实例第…

Python -- machine learning， neural network -- PyBrain 机器学习神经网络

I am using pybrain on my Linuxmint 13 x86_64 PC. As what it is described: PyBrain is a modular Machine Learning Library for Python. Its goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of p…

machine learning 之 Neural Network 1

整理自Andrew Ng的machine learning课程week 4. 目录: 为什么要用神经网络神经网络的模型表示 1 神经网络的模型表示 2 实例1 实例2 多分类问题 1.为什么要用神经网络当我们有大量的features时:如$x_1, x_2,x_3.......x_{100}$ 假设我们现在使用一个非线性的模型,多项式最高次为2次,那么对于非线性分类问题而言,如果使用逻辑回归的话: $g(\theta_0+\theta_1x_1+\theta_2x_2+\theta_3x_1…

machine learning 之 Neural Network 2

整理自Andrew Ng的machine learning 课程 week5. 目录: Neural network and classification Cost function Backpropagation (to minimize cost function) Backpropagation in practice Gradient checking Random initialization Assure structure and Train a neural network 前提…

machine learning 之 Neural Network 3

整理自Andrew Ng的machine learning课程week6. 目录: Advice for applying machine learning (Decide what to do next) Debugging a learning algorithm machine learning diagnostic Evaluating a hypothesis Model selection and Train / validation / test set Bias and Vari…

【【MetaPruning】2019-ICCV-MetaPruning Meta Learning for Automatic Neural Network Channel Pruning-论文阅读】的更多相关文章