前言 最近有关于3DMM的内容,博主也只是看了个大概,并没有深入了解算法的实现原理和过程.昨天实习生问关于黄金标准算法的推导,博主也就参考一些资料熟悉了这个算法的实现过程.不太了解使用这个算法的前因后果的可参考here,另外,过程中发现一本书关于计算机视觉中的多视图几何(Multiple View Geometry in Computer Vision)的. 算法目标 前因请参考here,这里该算法是为了估计二维和三维坐标点之间的对应关系,通过这个仿射变换关系可以分解推导出缩放因子.旋转矩阵和平

Turtle Talk Prior to the on-set motion capture, the team had the actors perform expressions while being scanned with Disney Research's Medusa system. The Muse team decomposed those scans into components within Fez, ILM's facial animation system that

http://www.ics.uci.edu/~dramanan/teaching/ics273a_winter08/lectures/lecture14.pdf Loss Function 损失函数可以看做 误差部分(loss term) + 正则化部分(regularization term) 1.1 Loss Term Gold Standard (ideal case) Hinge (SVM, soft margin) Log (logistic regression, cross en

Source: http://mindhive.mit.edu/book/export/html 1. What is the multiple-comparison problem? What is familywise error correction (FWE)? To start, Nichols and Hayasaka (PthresholdPapers) provide an excellent introduction to the issue of FWE in neuroim

https://en.wikipedia.org/wiki/Named-entity_recognition http://book.51cto.com/art/201107/276852.htm 命名实体(Named Entity)类别识别 除了在预测用户意图方面的用途,查询日志还可以用来识别命名实体.命名实体识别是指识别文本中具有特定意义的实体,主要包括人名.地名.机构名.时 间.日期.货币及其他专有名词等.它是自然语言处理实用化的重要内容,在信息提取.句法分析.机器翻译等应用领域中具有重要

Introduction One of the greatest logging tools out there for .NET is log4net. This software is the gold standard for how logging should be done. It is simple, powerful, and extensible. The best part is that it is part of the FOSS community. What coul

https://www.oreilly.com/ideas/the-world-beyond-batch-streaming-101 https://www.oreilly.com/ideas/the-world-beyond-batch-streaming-102   这篇文章,首先要说清的一个问题是,给'Streaming'正名 What is streaming? The crux of the problem is that many things that ought to be de

What is Text Classification? Text classification typically involves assigning a document to a category by automated or human means. LingPipe provides a classification facility that takes examples of text classifications--typically generated by a huma

[machine learning] Loss Function view 有关Loss Function(LF),只想说,终于写了 一.Loss Function 什么是Loss Function?wiki上有一句解释我觉得很到位,引用一下:The loss function quantifies the amount by which the prediction deviates from the actual values.Loss Function中文损失函数,适用于用于统计,经济,机

如果利用opencv里面提供的stitching detail的话. 输入参数: stitching_detail --save_graph a.dot 1.png 2.png 其中a.dot 文件中的内容如下: graph matches_graph{ "1.png" -- "2.png"[label="Nm=26, Ni=19, C=1.20253"]; } // 什么是 dot:    https://zh.wikipedia.org/wi

linear regression logistic regression softmax regression #@author: gr #@date: 2014-01-21 #@email: forgerui@gmail.com 一.linear regression 线性模型: \[h_\theta(x) = \theta^T X\] 代价函数: 代价函数使用平方误差损失函数. \[\min_\theta J(\theta) = \dfrac{1}{2} \sum_{i=1}^m(h_\t

感谢原文作者!原文地址:http://eletva.com/tower/?p=186 一.Loss Function 什么是Loss Function?wiki上有一句解释我觉得很到位,引用一下:The loss function quantifies the amount by which the prediction deviates from the actual values.Loss Function中文损失函数,适用于用于统计,经济,机器学习等领域,虽外表形式不一,但其本质作用应是唯

目录 1. 问题 2. 方法 3. 实验设计 3.1. 解决词典内部(一组已知)任务的能力 3.2. 解决新任务(少量标记数据)的能力 4. 讨论和启发 论文:Taskonomy: Disentangling Task Transfer Learning Zamir, Amir R., et al. "Taskonomy: Disentangling task transfer learning." Proceedings of the IEEE Conference on Compu

  CONTENT: example : KDE bug report: https://bugs.kde.org/show_bug.cgi?id=188311 (其中还有很多comments没显示) 构建分类器,对comments中的每一句话(sentence)进行二分类.其中,0代表不选入summary,1代表选入summary. 最终,生成对bug report的答案: 研究问题: 实验方法: 1.找一帮人(10个人),对5个开源项目(Eclipse,Platform,Gnome,Mozi

来自论文:GENIA corpus—a semantically annotated corpus for bio-textmining  2003 1.介绍 GENIA corpus, a semantically annotated corpus of biological literature, is being compiled and annotated in the scope of GENIA project. It is aiming at providing high qual

翻译 Improved Word Representation Learning with Sememes 题目 Improved Word Representation Learning with Sememes 融合义原知识的词汇表示学习 摘要 Abstract Sememes are minimum semantic units of word meanings, and the meaning of each word sense is typically composed by sev

这次作业叫Text Analysis and Entity Resolution,比前几次作业难度要大很多.相关ipynb文件见我github. 实体解析在数据清洗和数据整合中是一个很重要,且有难度的问题.这次作业将用Apache Spark和文本分析的方法应用到实体解析.实体解析是指,从不同的数据源的记录里找到相同的实体,当进行数据融合时,这个步骤是必须的. 这次作业的数据来源于metric-learning project,主要目录包括: Google.csv, the Google Pro

原文连接 https://blog.csdn.net/guolindonggld/article/details/56966200 1. 简介 BLEU(Bilingual Evaluation Understudy),相信大家对这个评价指标的概念已经很熟悉,随便百度谷歌就有相关介绍.原论文为BLEU: a Method for Automatic Evaluation of Machine Translation,IBM出品. 本文通过一个例子详细介绍BLEU是如何计算以及NLTKnltk.a

http://blog.csdn.net/guixunlong/article/details/8925990 从头开始编写基于隐含马尔可夫模型HMM的中文分词器之一 - 资源篇 首先感谢52nlp的系列博文(http://www.52nlp.cn/),提供了自然语言处理的系列学习文章,让我学习到了如何实现一个基于隐含马尔可夫模型HMM的中文分词器. 在编写一个中文分词器前,第一步是需要找到一些基础的词典库等资源,用以训练模型参数,并进行后续的结果评测,这里直接转述52nlp介绍的“中文分词入门

如何提高深度学习性能 20 Tips, Tricks and Techniques That You Can Use ToFight Overfitting and Get Better Generalization How can you get better performance from your deep learning model? It is one of the most common questions I get asked. It might be asked as: H