dmlc.cs.washington.edu dmlc.cs.washington.edu

dmlc.cs.washington.edu

DMLC UW

DMLC UW project sites

http://dmlc.cs.washington.edu/

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DMLC UW | dmlc.cs.washington.edu Reviews

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DMLC UW project sites

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1

Rabit

http://dmlc.cs.washington.edu/rabit.html

Rabit: Reliable Allreduce and Broadcast Interface. Rabit is a light weight library that provides a fault tolerant interface of Allreduce and Broadcast. It is designed to support easy implementations of distributed machine learning programs, many of which fall naturally under theAllreduce abstraction. The goal of rabit is to support. Distributed machine learning programs. The newest version can also be used within DataFlow frameworks such as Flink and Spark. Rabit is light weight and runs everywhere.

2

MXNet

http://dmlc.cs.washington.edu/mxnet.html

MXNet: Efficient and Flexible Deep Learning. This is a project created in collaboration with researchers from CMU, NYU, NUS, MIT. And developed with many others. Open Source Design Notes on Deep Learning Systems. Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. MShadow: A Unified CPU/GPU Matrix Template Library in C /CUDA. In order to supp...

3

People

http://dmlc.cs.washington.edu/people.html

This is the page listing researchers who collaborate in DMLC projects at University of Washington. Tianqi is creator of many DMLC projects, including XGBoost. He is also initiator and major contributor of MXNet. Eric is a Ph.D. student working on computer vision. He is major contributor of MXNet. Large-scale machine learning at the University of Washington.

4

XGBoost

http://dmlc.cs.washington.edu/xgboost.html

XGBoost: A Scalable Tree Boosting System. XGBoost is an optimized distributed gradient boosting system designed to be highly efficient. Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. Sparse aware tree learning. To optimize for sparse data. Distributed weighted quantile sketch. For quantile findings and approximate tree learning. Cache aware learning algorithm. Out of core computation system. XGBoost is one of the most frequently used package to win machine learning challenges.

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DMLC UW

Large-Scale Machine Learning at UW. Machine learning and data-driven approaches are becoming very important in many areas. It is increasing important to make scalable machine learning solutions. For learning This is the project page about this joint effort at University of Washington. Most of the projects listed in this page is also part of Distributed Machine Learning Common. XGBoost: A Scalable Tree Boosting System. And machine learning engineers in industry. MXNet: Efficient and Flexible Deep Learning.

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DMLC

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