图 书 馆开课单位--微软公司
31
32
33
34
35
36
37
38
39
40
31FPGA-based MapReduce Framework for Machine Learning[一种针对机器学习而基于现场可编程门阵列的映射归约框架]
Ningyi Xu(微软公司) Machine learning algorithms are becoming increasingly important in our daily life. However, training on very large scale datasets is usually very slow...
热度：32
Ningyi Xu(微软公司) Machine learning algorithms are becoming increasingly important in our daily life. However, training on very large scale datasets is usually very slow...
热度：32
32A Novel Click Model and Its Applications to Online Advertising[一种新的点击模型及其应用于在线广告]
Weizhu Chen(微软公司) Recent advances in click model have positioned it as an attractive method for representing user preferences in web search and online advertising. Yet,...
热度：65
Weizhu Chen(微软公司) Recent advances in click model have positioned it as an attractive method for representing user preferences in web search and online advertising. Yet,...
热度：65
33Broadening statistical machine translation with comparable corpora and generalized models[扩大统计机器翻译与可比语料库和广义模型]
Chris Quirk(微软公司) As we scale statistical machine translation systems to general domain, we face many challenges. This talk outlines two approaches for building better ...
热度：45
Chris Quirk(微软公司) As we scale statistical machine translation systems to general domain, we face many challenges. This talk outlines two approaches for building better ...
热度：45
34Fighting Spam under Attack: Some Notes from the Field[打击垃圾邮件的攻击下：一些笔记从现场]
Aleksander Kołcz(微软公司)
热度：52
Aleksander Kołcz(微软公司)
热度：52
35Scale-out Beyond MapReduce[超越MapReduce的横向扩展]
Raghu Ramakrishnan(微软公司) The amount of data being collected is growing at a staggering pace. The default is to capture and store any and all data, in anticipation of potential...
热度：31
Raghu Ramakrishnan(微软公司) The amount of data being collected is growing at a staggering pace. The default is to capture and store any and all data, in anticipation of potential...
热度：31
36Diversifying Search Results[多样化的搜索结果]
Sreenivas Gollapudi; Alan Halverson; Samuel Ieong; Rakesh Agrawal(微软公司)
热度：32
Sreenivas Gollapudi; Alan Halverson; Samuel Ieong; Rakesh Agrawal(微软公司)
热度：32
37Self-calibrating Photometric Stereo[自动校准光度计]
Yasuyuki Matsushita(微软公司) We present a self-calibrating photometric stereo method. From a set of images taken from a fixed viewpoint under different and unknown lighting condit...
热度：209
Yasuyuki Matsushita(微软公司) We present a self-calibrating photometric stereo method. From a set of images taken from a fixed viewpoint under different and unknown lighting condit...
热度：209
38Office.com 2010: Re-engineering for Global reach and local touch[Office.com2010：重新设计的全球影响力和本地触摸]
Dag Schmidtke(微软公司) Office.com is one of the largest multilingual content driven web-sites in the world. With more than 1 billion visits per year, it reaches 40 languages...
热度：48
Dag Schmidtke(微软公司) Office.com is one of the largest multilingual content driven web-sites in the world. With more than 1 billion visits per year, it reaches 40 languages...
热度：48
3940Non-ParametricCRFs for Image Labeling[图像标签的非CRF参数]
Jeremy Jancsary(微软公司) We introduce a powerful non-parametric image labeling framework, Regression Tree Fields (RTFs), and discuss its application to image restoration. The ...
热度：45
Jeremy Jancsary(微软公司) We introduce a powerful non-parametric image labeling framework, Regression Tree Fields (RTFs), and discuss its application to image restoration. The ...
热度：45