Matchbox: Large Scale Bayesian Recommendations Matchbox: Large Scale Bayesian Recommendations David Stern, Ralf Herbrich, and Thore Graepel 2009 We present a probabilistic model for generating personalised recommendations of items to users of a web service. The Matchbox system makes use of content information in the form of user and item meta data in combination with collaborative filtering information from previous user behavior in order to predict the value of an item for a user. Users and items are represented by feature vectors which are mapped into a low-dimensional ‘trait space’ in which similarity is measured in terms of inner products. In Proceedings of the 18th International World Wide Web Conference
Windows Marketplace Office 365 University ★★★★★ ★★★★★ 3.7 out of 5 stars. Read reviews. Get a subscription including Word, Excel, PowerPoint, and Outlook, OneDrive cloud storage, and Skype PC-to-phone world calling per month for 2 PCs, Macs or tablets.4 Student price Nike Running Machine learning for dummies - Next at Microsoft [image courtesy of Ariel Stallings] Say machine learning to most people and they’ll look at you suspiciously – even people in the tech industry have a degree of caution is it conjures up notions of machines taking over the world in some sort of Terminator/singularity way. I think it’s because people translate it in to “machines that learn” which as it turns out, is a perfectly good way to think about this topic. I decided to dig a little deep and get past the jargon as I think it’s an increasingly important field and will help unlock the potential for natural user interfaces and technology that anticipates our needs and feels more human. “That represents the real Oscar I won” is John’s sheepish response. “Yes, it’s a technical academy award I got for research in to how you represent flexible objects in movies in a simulated way – the kind of stuff you regularly see in movies from Pixar and the like” So I add Oscar winner to the list of people we have working at the company.
Microsoft - At Work Sphinx-4 - A speech recognizer written entirely in the Java(TM) programming language Overview Sphinx4 is a pure Java speech recognition library. It provides a quick and easy API to convert the speech recordings into text with the help CMUSphinx acoustic models. It can be used on servers and in desktop applications. Beside speech recognition Sphinx4 helps to identify speakers, adapt models, align existing transcription to audio for timestamping and more. Sphinx4 supports US English and many other languages. Using in your projects As any library in Java all you need to do to use sphinx4 is to add jars into dependencies of your project and then you can write code using the API. The easiest way to use modern sphinx4 is to use modern build tools like Apache Maven or Gradle. <project> ... Then add sphinx4-core to the project dependencies: <dependency><groupId>edu.cmu.sphinx</groupId><artifactId>sphinx4-core</artifactId><version>5prealpha-SNAPSHOT</version></dependency> Add sphinx4-data to dependencies as well if you want to use default acoustic and language models: Basic Usage or
A Programming Language for DNA Computing Recently, a range of information-processing circuits have been implemented in DNA by using strand displacement as their main computational mechanism. Examples include digital logic circuits and catalytic signal amplification circuits that function as efficient molecular detectors. As new paradigms for DNA computation emerge, the development of corresponding languages and tools for these paradigms will help to facilitate the design of DNA circuits and their compilation to nucleotide sequences. We present a programming language for designing and simulating DNA circuits in which strand displacement is the main computational mechanism. The language includes basic elements of sequence domains, toeholds and branch migration, and assumes that strands do not possess any secondary structure.
Microsoft - At Home LIBSVM -- A Library for Support Vector Machines LIBSVM -- A Library for Support Vector Machines Chih-Chung Chang and Chih-Jen Lin Version 3.20 released on November 15, 2014. It conducts some minor fixes. LIBSVM tools provides many extensions of LIBSVM. We now have a nice page LIBSVM data sets providing problems in LIBSVM format. A practical guide to SVM classification is available now! To see the importance of parameter selection, please see our guide for beginners. Using libsvm, our group is the winner of IJCNN 2001 Challenge (two of the three competitions), EUNITE world wide competition on electricity load prediction, NIPS 2003 feature selection challenge (third place), WCCI 2008 Causation and Prediction challenge (one of the two winners), and Active Learning Challenge 2010 (2nd place). Introduction LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). Our goal is to help users from other fields to easily use SVM as a tool.