这里对WWW 2017文章<Beyond Globally Optimal: Focused Learning for Improved Recommendations>进行一个简单的分析解读. 文章亮点: 作者群来自Google和Pinterest 第一次系统性的解释了为什么用全局模型可能会在局部数据上表现不好 文章提出了简单易行的解决办法,可以让全局模型在局部数据上依然能够达到最优 文章很全面得在一些数据及上进行了比较完整的比较 这篇文章来自一群前CMU的学者,目前在Google和Pint…

Machine and Deep Learning with Python Education Tutorials and courses Supervised learning superstitions cheat sheet Introduction to Deep Learning with Python How to implement a neural network How to build and run your first deep learning network Neur…

读了一篇paper,MSRA的Wei Wu的一篇<Learning Query and Document Similarities from Click-through Bipartite Graph with Metadata>.是关于Ranking Relevence方面的文章.下面简单讲下我对这篇文章的理解,对这方面感兴趣的小伙伴们可以交流一下. 1. Abstract 这篇文章的重点在于使用query-doc的点击二部图,结合query/doc的meta数据(组织成multiple t…

http://www.personal.kent.edu/~rmuhamma/Algorithms/MyAlgorithms/Greedy/greedyIntro.htm Greedy Introduction Greedy algorithms are simple and straightforward. They are shortsighted in their approach in the sense that they take decisions on the basis of…

论文信息 论文标题:Data Augmentation for Deep Graph Learning: A Survey论文作者:Kaize Ding, Zhe Xu, Hanghang Tong, Huan Liu论文来源:2022, arXiv论文地址:download 1 介绍 本文主要总结图数据增强,并对该领域的代表性方法做出归类分析. DGL 存在的两个问题: 次优图问题:图中包含不确定.冗余.错误和缺失的节点特征或图结构边. 有限标签问题:标签数据成本高,目前大部分 DGL 方法是…

@http://www-cs-faculty.stanford.edu/people/karpathy/cvpr2015papers/ CVPR 2015 papers (in nicer format than this) maintained by @karpathy NEW: This year I also embedded the (1,2-gram) tfidf vectors of all papers with t-sne and placed them in an interf…

http://www.cv-foundation.org/openaccess/CVPR2016.py ORAL SESSION Image Captioning and Question Answering Monday, June 27th, 9:00AM - 10:05AM. These papers will also be presented at the following poster session 1   Deep Compositional Captioning: Descr…

Accepted Papers     Title Primary Subject Area ID 3D computer vision 93 UPnP: An optimal O(n) solution to the absolute pose problem with universal applicability 128 Video Registration to SfM Models 168 Image-based 4-d Modeling Using 3-d Change Detect…

CVPR2015 Papers震撼来袭! CVPR 2015的文章可以下载了,如果链接无法下载,可以在Google上通过搜索paper名字下载(友情提示:可以使用filetype:pdf命令). Going Deeper With ConvolutionsChristian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke…

新手入门完整教程进阶指南 API中文手册精华文章TF社区 INTRODUCTION 1. 新手入门 1.1. 介绍 1.2. 下载及安装 1.3. 基本用法 2. 完整教程 2.1. 总览 2.2. MNIST 数据下载 2.3. MNIST 入门 2.4. MNIST 进阶 2.5. TENSORFLOW 运作方式入门 2.6. 卷积神经网络 2.7. 字词的向量表示 2.8. 递归神经网络 2.9. 曼德布洛特(MANDELBROT)集合 2.10. 偏微分方程 3. 进阶指南 3.1. 总…

CVPR2016 Paper list ORAL SESSIONImage Captioning and Question Answering Monday, June 27th, 9:00AM - 10:05AM. These papers will also be presented at the following poster session 1 Deep Compositional Captioning: Describing Novel Object Categories Witho…

此主要讨论图像处理与分析.虽然计算机视觉部分的有些内容比如特 征提取等也可以归结到图像分析中来,但鉴于它们与计算机视觉的紧密联系,以 及它们的出处,没有把它们纳入到图像处理与分析中来.同样,这里面也有一些 也可以划归到计算机视觉中去.这都不重要,只要知道有这么个方法,能为自己 所用,或者从中得到灵感,这就够了. 8. Edge Detection 边缘检测也是图像处理中的一个基本任务.传统的边缘检测方法有基于梯度 算子,尤其是 Sobel 算子,以及经典的 Canny 边缘检测.到现在,Cann…

From:  http://www.pamitc.org/cvpr15/program.php Official Program for CVPR 2015 Monday, June 8 8:30am-8:40am Ballrooms A,B,C Rooms 302,304,306 Opening Remarks from Conference Chairs The opening remarks will be made from Ballrooms A,B,C, but a live vid…

Delaunay Triangulation in OpenCascade eryar@163.com 摘要:本文简要介绍了Delaunay三角剖分的基础理论,并使用OpenCascade的三角剖分算法将边界BRep表示的几何体进行三角离散化后在OpenSceneGraph中显示. 关键字:Delaunay Triangulation.OpenCascade.OpenSceneGraph 一. 概述 三角剖分是平面剖分中的一个重要课题,在数字图像处理.计算机三维曲面造型.有限元计算.逆向工程等领…

2013年暑期买到这本书,距离其第一版已经有14年之久,而最早发布在互联网上的文章更是早在1997年.在我阅读的时候,很多事迹已经沉积为历史,很多预言已经成为现实.而这本书的意义却丝毫没有因此淡化,反而更加支持了书里的立论. <大教堂与集市>作者 Eric Raymond, 第一次知道此人还是在天津大学的操作系统原理课程上.大教堂与集市,分别代表两种软件开发模式,大教堂代表着封闭的决策权集中的方式,集市代表开放的,平等透明,共享互动的开源软件开发方式. 本篇博文,主要就一些点展开一些讨论,评价…

https://www.quora.com/Why-do-people-use-gradient-boosted-decision-trees-to-do-feature-transform Why is linearity/non-linearity important?Most of our classification models try to find a single line that separates the two sets of point. I say that they…

​蝶恋花·槛菊愁烟兰泣露 槛菊愁烟兰泣露,罗幕轻寒,燕子双飞去. 明月不谙离恨苦,斜光到晓穿朱户. 昨夜西风凋碧树,独上高楼,望尽天涯路. 欲寄彩笺兼尺素.山长水阔知何处? --晏殊 导读: 3D点云配准是计算机视觉的关键研究问题之一,在多领域工程应用中具有重要应用,如逆向工程.SLAM.图像处理和模式识别等.点云配准的目的是求解出同一坐标下不同姿态点云的变换矩阵,利用该矩阵实现多视扫描点云的精确配准,最终获取完整的3D数字模型.场景.本质上,关于六自由度(旋转和平移)的3D点云配准问题是典型的…

本文翻译自 Yizhi Liu, Yao Wang, Ruofei Yu.. 的  "Optimizing CNN Model Inference on CPUs" 原文链接: https://arxiv.org/abs/1809.02697 翻译:coneypo,working in Intel for IoT 这篇文章介绍了基于 TVM 改进的 NeoCPU 方案,在 CPU 上进行 CNN 模型推理优化: 与之对比是 Intel 的 OpenVINO 版本(2018.5 ,最新的…

透视 n 点问题,源自相机标定,是计算机视觉的经典问题,广泛应用在机器人定位.SLAM.AR/VR.摄影测量等领域 1  PnP 问题 1.1  定义 已知:相机的内参和畸变系数:世界坐标系中,n 个空间点坐标,以及投影在像平面上的像素坐标 求解:相机在世界坐标系下的位姿 R 和 t,即 {W} 到 {C} 的变换矩阵 $\;^w_c\bm{T} $,如下图: 世界坐标系中的 3d 空间点,与投影到像平面的 2d 像素点,两者之间的关系为: $\quad s \begin{bmatrix} u…

1-1 Introduction to the focused and diffuse modes (4:40) 两种思考的模式:focused mode以及diffuse mode focused mode:是当有意识地专注思考问题或理解概念时的大脑工作模式.大脑的某些部分高强度的工作,但仅限于这一小部分. diffuse mode: 是大脑神经处于某种放松状态的模式.如果处理没有处理过的问题,需要新的想法,那么你的大脑找不到focused mode中存在的那部分固定的思考模式相关的神经,这就…

http://www.mit.edu/~9.54/fall14/slides/Reinforcement%20Learning%202-Model%20Free.pdf [基于所有.单个样本]…

ON THE EVOLUTION OF MACHINE LEARNING: FROM LINEAR MODELS TO NEURAL NETWORKS We recently interviewed Reza Zadeh (@Reza_Zadeh). Reza is a Consulting Professor in the Institute for Computational and Mathematical Engineering at Stanford University and a…

The Brain vs Deep Learning Part I: Computational Complexity — Or Why the Singularity Is Nowhere Near July 27, 2015July 27, 2015 Tim Dettmers Deep Learning, NeuroscienceDeep Learning, dendritic spikes, high performance computing, neuroscience, singula…

Machine Learning Crash Course  |  Google Developers https://developers.google.com/machine-learning/crash-course/ Google's fast-paced, practical introduction to machine learning ML Concepts Introduction to Machine Learning As you'll discover, machine…

转自:http://www.cleverhans.io/security/privacy/ml/2017/02/15/why-attacking-machine-learning-is-easier-than-defending-it.html   Is attacking machine learning easier than defending it? Feb 15, 2017 by Ian Goodfellow and Nicolas Papernot In our first post…

https://stats385.github.io/readings Lecture 1 – Deep Learning Challenge. Is There Theory? Readings Deep Deep Trouble Why 2016 is The Global Tipping Point... Are AI and ML Killing Analyticals... The Dark Secret at The Heart of AI AI Robots Learning Ra…

https://www.quora.com/How-do-I-learn-mathematics-for-machine-learning   How do I learn mathematics for machine learning? Promoted by Time Doctor Software for productivity tracking. Time tracking and productivity improvement software with screenshots…

What is AngularJS? Angular is a client-side MVC/MVVM framework built in JavaScript, essential for modern single page web applications (and even websites). This post is a full end to end crash course from my experiences, advice and best practices I've…

ICLR 2013 International Conference on Learning Representations May 02 - 04, 2013, Scottsdale, Arizona, USA ICLR 2013 Workshop Track Accepted for Oral Presentation Zero-Shot Learning Through Cross-Modal Transfer Richard Socher, Milind Ganjoo, Hamsa Sr…

ICLR 2014 International Conference on Learning Representations Apr 14 - 16, 2014, Banff, Canada Workshop Track Submitted Papers Stochastic Gradient Estimate Variance in Contrastive Divergence and Persistent Contrastive Divergence Mathias Berglund, Ta…