Problem: get an overall picture of how ego-networks evolve is a common challenging task. Existing techniques: inspect the evolution patterns of ego-networks one after another. Purpose: how analysts can gain insights into the overall evolution pattern…

Classifying plankton with deep neural networks The National Data Science Bowl, a data science competition where the goal was to classify images of plankton, has just ended. I participated with six other members of my research lab, the Reservoir lab o…

上一篇我们介绍了:深度学习方法(十二):卷积神经网络结构变化--Spatial Transformer Networks,STN创造性地在CNN结构中装入了一个可学习的仿射变换,目的是增加CNN的旋转.平移.缩放.剪裁性.为什么要做这个很奇怪的结构呢?原因还是因为CNN不够鲁棒,比如把一张图片颠倒一下,可能就不认识了(这里mark一下,提高CNN的泛化能力,值得继续花很大力气,STN是一个思路,读者以及我自己应该多想想,还有什么方法?). 今天介绍的这一篇可变形卷积网络deformable co…

注意力机制之Attention Augmented Convolutional Networks 原始链接:https://www.yuque.com/lart/papers/aaconv 核心内容 We propose to augment convolutional operators with this self-attention mechanism by concatenating convolutional feature maps with a set of feature map…

CVPR2017 paper list Machine Learning 1 Spotlight 1-1A Exclusivity-Consistency Regularized Multi-View Subspace Clustering Xiaojie Guo, Xiaobo Wang, Zhen Lei, Changqing Zhang, Stan Z. Li Borrowing Treasures From the Wealthy: Deep Transfer Learning Thro…

转:http://www.zhizhihu.com/html/y2011/3228.html l  Theory n  Introduction u  Unsupervised learning by probabilistic latent semantic analysis. u  Latent dirichlet allocation. u  Finding scientific topics. u  Rethinking LDA: Why Priors Matter u  On an e…

Accepted Papers by Session Research Session RT01: Social and Graphs 1Tuesday 10:20 am–12:00 pm | Level 3 – Ballroom AChair: Tanya Berger-Wolf Efficient Algorithms for Public-Private Social NetworksFlavio Chierichetti,Sapienza University of Rome; Ales…

大致步骤: 1.Java bean 2.DBHelper.java 3.重写DefaultHandler中的方法:MyHander.java 4.循环写数据库:SAXParserDemo.java ①xml文件:(要把第二行dtd的绑定删掉) 1 <?xml version="1.0" encoding="utf-8" ?> 2 <!DOCTYPE dblp SYSTEM "dblp.dtd"> 3 <dblp>…

版权声明:本文为博主原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接和本声明. 本文链接:https://blog.csdn.net/u010167269/article/details/52563573 Preface 这是今年 ECCV 2016 的一篇文章,是 UNC Chapel Hill(北卡罗来纳大学教堂山分校) 的 Wei Liu 大神的新作,论文代码:https://github.com/weiliu89/caffe/tree/ssd 有几点更新: 1. 看…

此部分是计算机视觉部分,主要侧重在底层特征提取,视频分析,跟踪,目标检测和识别方面等方面.对于自己不太熟悉的领域比如摄像机标定和立体视觉,仅仅列出上google上引用次数比较多的文献.有一些刚刚出版的文章,个人非常喜欢,也列出来了. 33. SIFT关于SIFT,实在不需要介绍太多,一万多次的引用已经说明问题了.SURF和PCA-SIFT也是属于这个系列.后面列出了几篇跟SIFT有关的问题.[1999 ICCV] Object recognition from local scale-invar…

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…

pingback :http://java.sys-con.com/node/84633?page=0,1 Object-oriented design is like an alloy consisting of a solid grounding in the object-oriented (OO) approach and implementing the best OO practices heavily laced with how to sidestep the OO pitfal…

0.1 TopicNotes of Lin C., Snyder L.. Principles of Parallel Programming. Beijing: China Machine Press. 2008. (1) Parallel Computer Architecture - done 2015/5/24(2) Parallel Abstraction - done 2015/5/28(3) Scable Algorithm Techniques - done 2015/5/30(…

Nine Great Books about Information Visualization Maybe it’s anachronistic to celebrate static, printed books when so many of us love and create interactive data displays. I don’t care. I love books. Edward Tufte, the patron saint of information visua…

1. Parameter pruning and sharing 1.1 Quantization and Binarization Compressing deep convolutional networks using vector quantization Quantized convolutional neural networks for mobile devices Improving the speed of neural networks on cpus Deep learni…

http://www.cs.utexas.edu/~grauman/ CV         Publications         Code           Data        Short Bio         Press I am an Associate Professor in the Department of Computer Science at the University of Texas at Austin, where I lead the UT-Austin C…

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. 注意:Registration可翻译为“配准”或“匹配”,一般是图像配准,特征匹配(特征点匹配). MIA] Image matching as a diffusion process[…

Course descriptionWith the continuing advances of geographic information science and geospatialtechnologies, spatially referenced information have been easily and increasinglyavailable in the past decades and becoming important information sources in…

Purpose Implement a good user aggregation and classification. or to assess the interrelation patterns between user profiles. Data i. daily temperature and load profiles sampled at hourly for one year. ii. 2201 customers iii. Methodology 1. correlatio…

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…

Improvement can be done in fulture:1. the algorithm of constructing network from distance matrix. 2. evolution of sliding time window3. the later processing or visual analysis of generated graphs. Thinking: 1.What's the ground truth in load profiles?…

SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks 2019-04-02 12:44:36 Paper:https://arxiv.org/pdf/1812.11703.pdf Project:https://lb1100.github.io/SiamRPN++ 1. Background and Motivation: 与 CVPR 2019 的另一篇文章 Deeper and Wider Siames…

Problem: time series classification shapelet-based method: two issues 1. for multi-class imbalanced classification tasks, these methods will ignore the shapelets that can distinguish minority class from other classes. 2. the shapelets are fixed after…

Purpose detect the dynamics in time series of their correlation Methodology 1. calculate correlation coefficients 2. Network construction: i. node: AAB, ABC, etc. totally 5^3=125个nodes. 但是不是所有的mode情况都会出现,于是the number of nodes 是个随机数, 并且满足<125. ii. edg…

From google institution; 1. Before this, DNN cannot be used to map sequences to sequences. In this paper, we propose a sequence learning that makes minimal assumptions on the sequence structure. use lstm to map the input sequence to a vector of a fix…

FROM Amazon research Germany PROBLEM probabilistic forecasting: estimate the probability distribution of a time series in future. INTRODUCTION a global model, which learns from historical data of all time series. METHOD an autoregressive recurrent ne…

The 10th international conference on machine vision; C类 Methodology: 非主流方法 2 stages: 1. convert time series data to recurrence plot. 数值*时间长度----------> 时间长度*时间长度. 2. fed into CNN model. 潜在问题: 1. 由time series data 转化成为 recurrence plot是否丢失了信息,丢失了哪些信息--…

w可以考虑从计算机的“机械性.重复性”特征去设计“低效的”算法. https://www.codeproject.com/articles/523074/webcontrols/ Online handwriting recognition using multi convolution neural networks Vietdungiitb, 13 Jan 2013 CPOL This article has been presented at The Ninth International…

Attention and Augmented Recurrent Neural Networks CHRIS OLAHGoogle Brain SHAN CARTERGoogle Brain Sept. 8 2016 Citation: Olah & Carter, 2016 Recurrent neural networks are one of the staples of deep learning, allowing neural networks to work with seque…

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 every…