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Advances in Algorithms and Data Management. Venue: Emerald Bay B, Harveys. Welcome to the webpage for NIPS 2013. Workshop on parallel and large-scale machine learning. Explosive growth in data and availability of cheap computing resources has sparked increasing interest in Big Learning within the Machine Learning community. Researchers are now taking on the challenge of parallelizing richly structured models with inherently serial dependencies and do not admit straightforward solutions. Introduce DB impl...

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Big Learning Main/Home Page | biglearn.org Reviews

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Advances in Algorithms and Data Management. Venue: Emerald Bay B, Harveys. Welcome to the webpage for NIPS 2013. Workshop on parallel and large-scale machine learning. Explosive growth in data and availability of cheap computing resources has sparked increasing interest in Big Learning within the Machine Learning community. Researchers are now taking on the challenge of parallelizing richly structured models with inherently serial dependencies and do not admit straightforward solutions. Introduce DB impl...

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5 keynote talks

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Big Learning Main/Home Page | biglearn.org Reviews

https://biglearn.org

Advances in Algorithms and Data Management. Venue: Emerald Bay B, Harveys. Welcome to the webpage for NIPS 2013. Workshop on parallel and large-scale machine learning. Explosive growth in data and availability of cheap computing resources has sparked increasing interest in Big Learning within the Machine Learning community. Researchers are now taking on the challenge of parallelizing richly structured models with inherently serial dependencies and do not admit straightforward solutions. Introduce DB impl...

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biglearn.org

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Big Learning Main/Dates

http://biglearn.org/index.php/Dates

Advances in Algorithms and Data Management. The dates below are subject to change. Call for papers announced:. August 25th, 2013. October 9th, 2013. October 25th, 2013. November 8th, 2013. November 15th, 2013. December 9th, 2013. Designed and maintained by Haijie Gu and Sameer Singh. Page last modified on November 14, 2013, at 02:19 AM. Theme originally by styleshout. Adapted by David Gilbert.

2

Big Learning Main/Home Page

http://biglearn.org/index.php

Advances in Algorithms and Data Management. NIPS 2015 Workshop on Machine Learning Systems ( LearningSys. The popular Big Learning workshop returns this year at NIPS 2015. In Montreal. In this new version, the workshop will focus more on systems designed for efficient machine learning, check it out and submit something here: http:/ learningsys.org. Venue: Emerald Bay B, Harveys. Welcome to the webpage for NIPS 2013. Workshop on parallel and large-scale machine learning. This workshop aims to bring togeth...

3

Big Learning Main/Organizers

http://biglearn.org/index.php/Organizers

Advances in Algorithms and Data Management. For any questions regarding the workshop, email the organizers at organizers@biglearn.org. Alexander J. Smola. Designed and maintained by Haijie Gu and Sameer Singh. Page last modified on September 01, 2013, at 06:02 PM. Theme originally by styleshout. Adapted by David Gilbert.

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Big Learning Main/Author Info

http://biglearn.org/index.php/AuthorInfo

Advances in Algorithms and Data Management. We suggest keeping the paper under 4 pages (NOT including references). For projects that require more room for descriptions, we encourage the authors to include details of the work as appendix and/or other supplementary materials. The submissions should include the authors' name and affiliation since the review process will not be double blind (use nipsfinalcopy. Accepted submissions will be presented either as contributed talks or as posters. Adapted by David ...

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Big Learning Main/Keynote Talks

http://biglearn.org/index.php/KeynoteTalks

Advances in Algorithms and Data Management. Michael I. Jordan. Joseph M. Hellerstein (Canceled). The Thorn in the Side of Big Data: Too Few Artists. Harvey's Emerald Bay B. Harvey's Emerald Bay B. In 2006 as a Yahoo! Timely dataflow in Naiad. Harvey's Emerald Bay B. For more information about Naiad, visit the project web page ( http:/ research.microsoft.com/naiad/. Or fork the project on Github ( https:/ github.com/MicrosoftResearchSVC/Naiad/. On the Computational and Statistical Interface and Big Data.

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select.cs.cmu.edu

SELECT Lab::News

http://select.cs.cmu.edu/news/index.html

Was awarded VMWare's graduate research fellowship. NIPS’11 Big Learning. We are organizing a Big Learning Workshop at NIPS this year! The Big Learning workshop will address the algorithms, systems, and tools, used in large-scale parallel and distributed learning problems. We encourage people interested in systems and learning at scale to attend and submit abstracts to be presented at this exciting two day workshop (see submission instructions. Best Research Paper Award. Best Student Paper Award. Is inten...

research.microsoft.com

Misha Bilenko

http://research.microsoft.com/en-us/um/people/mbilenko

I lead the Machine Learning Algorithms team in Cloud Enterprise division; we are a part of Microsoft Azure ML. Our learners, predictors and tools are used by many product groups across the company, and we collaborate extensively with MSR and applied ML/data science groups. If you love both ML fundamentals and coding, and would enjoy working on creating state-of-the-art ML systems with a fun group of excellent engineers and scientists, please ping me. At the University of Texas at Austin. Mikhail Bilenko,...

semanticpro.com

Semantic Pro - Semantic technologies for business solutions on the web

http://www.semanticpro.com/index.pl

Semantic Technologies for Web Solutions. We focus on building a bridge between academic research and practical needs in Semantic Technologies on the web. SemanticPRO Web Tools and Applications. Automated Submission Web Directory. Self-Expanding Web Directory Model. For accurate website categorization we use Machine Learning algorithms. No manual work is required to place a website link to the right place. This web application helps content writers get relevant and qualitative content which can boost web ...

blog.oliverparson.co.uk

Disaggregated Homes: Public Data Sets for NIALM

http://blog.oliverparson.co.uk/2012/06/public-data-sets-for-nialm.html

My name is Oliver Parson, and I'm currently employed as a Data Scientist at Centrica Connected Home. I'm interested in investigating the ways in which machine learning techniques can be used to break down household energy consumption data into individual appliances, also known as Non-intrusive Appliance Load Monitoring (NIALM) or energy disaggregation. Thursday, 21 June 2012. Public Data Sets for NIALM. Almanac of Minutely Power Dataset (AMPds). Berkeley Energy Disaggregation Data Set (BERDS). At the tim...

mlpack.org

mlpack: a scalable c++ machine learning library

http://www.mlpack.org/about.html

Is a C machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C language features to provide maximum performance and maximum flexibility for expert users. This is done by providing a set of command-line executables. Which can be used as black boxes, and a modular C API. As a result of this approach, mlpack. In your work using this citation. Gaussian m...

select.cs.cmu.edu

SELECT Lab::About

http://select.cs.cmu.edu/index.html

Welcome to the Select Lab. Our main long-term research goal is developing efficient algorithms and methods for designing, analyzing, and controlling complex real-world systems. To achieve this goal, our research spans the entire spectrum from theoretical foundations to real-world applications. Algorithmically, we focus on machine learning. Our main applications are sensor networks, computer systems and robotics. Was awarded VMWare's graduate research fellowship. NIPS’11 Big Learning. The Big Learning wor...

select.cs.cmu.edu

Large-Scale Machine Learning: Parallelism and Massive Datasets

http://www.select.cs.cmu.edu/meetings/biglearn09

Large-Scale Machine Learning: Parallelism and Massive Datasets. Friday December 11th from 7:30AM to 6:30PM. Hilton at Whistler in the Mt. Currie North Room. NIPS 2011 BigLearning Workshop! Bring together people with varying approaches to parallelism in machine learning to identify tools, techniques, and algorithmic ideas which have lead to successful parallel learning. Identify the next key challenges and opportunities to parallel learning. Prior NIPS workshops have focused on the topic of scaling machin...

bickson.blogspot.com

Large Scale Machine Learning and Other Animals: The world's coolest machine learning internships - updated

http://bickson.blogspot.com/2012/01/worlds-coolest-machine-learning.html

Large Scale Machine Learning and Other Animals. Wednesday, February 29, 2012. The world's coolest machine learning internships - updated. Note: Openings for summer 2014 are here. I believe that every graduate student should try to do at least two internships in the industry. It is a great experience. Below you can find a list I compiled by aggregating information from some of the companies I am in touch with as a part of our GraphLab project. Openings in the US - summer 2012. A well known Mahout contribu...

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biglearn.org

Big Learning Main/Home Page

Advances in Algorithms and Data Management. Venue: Emerald Bay B, Harveys. Welcome to the webpage for NIPS 2013. Workshop on parallel and large-scale machine learning. Explosive growth in data and availability of cheap computing resources has sparked increasing interest in Big Learning within the Machine Learning community. Researchers are now taking on the challenge of parallelizing richly structured models with inherently serial dependencies and do not admit straightforward solutions. Introduce DB impl...

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Home | Free Math and English Worksheets | Biglearners

Home of Free Worksheets. Biglearners.com is dedicated to provide high-quality educational materials for K-5 students and teachers. This site features thousands of printable Math and English language worksheets. Our collection includes spelling lists, grammar, reading comprehension passages, writing prompts, and flashcards. We have great learning resources in numbers, decimals, fractions, data and graphs, geometry, measurement, and many other topics. Full Screen PDFs - Always. Math and Language Content.

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