Add to ORKGImagine that you wish to classify data consisting of tens of thousands of examples residing in a twenty-thousand-dimensional space. How can one apply standard machine learning algorithms? We describe the Parallel Problem Server (PPServer) and MATLAB*P. In tandem they allow users of networked computers to work transparently on large data sets from within Matlab. This work is motivated by the desire to bring the many benefits of scientific computing algorithms and computational power to machine learning researchers.We demonstrate the usefulness of the system on a number of tasks. For example, we perform independent components analysis on very large text corpora consisting of tens of thousands of documents, making minimal changes to the origin...
MATLAB [7] is one of the most widely used mathematical computing environments in technical computing...
SystemML aims at declarative, large-scale machine learning (ML) on top of MapReduce, where high-leve...
When building large-scale machine learning (ML) programs, such as big topic models or deep neural ne...
This paper describes MITMatlab, a system that enables users of supercomputers or networked PCs to ...
The course offers basics of analyzing data with machine learning and data mining algorithms in order...
<p>Large scale machine learning has many characteristics that can be exploited in the system designs...
As ML applications are becoming ever more pervasive, fully-trained systems are made increasingly ava...
As Machine Learning (ML) applications are becoming ever more pervasive, fully-trained systems are ma...
Introduction We describe a novel architecture for a "linear algebra server" that operates...
Designing and implementing efficient, provably correct parallel machine learning (ML) algo-rithms is...
ABSTRACTThe rise of big data has led to new demands for machine learning (ML) systems to learn compl...
The rise of big data has led to new demands for machine learning (ML) systems to learn complex model...
: Machine learning using large data sets is a computationally intensive process. One technique that ...
How can one build a distributed framework that allows ef-ficient deployment of a wide spectrum of mo...
† These authors contributed equally. Machine learning (ML) and statistical techniques are key to tra...
MATLAB [7] is one of the most widely used mathematical computing environments in technical computing...
SystemML aims at declarative, large-scale machine learning (ML) on top of MapReduce, where high-leve...
When building large-scale machine learning (ML) programs, such as big topic models or deep neural ne...
This paper describes MITMatlab, a system that enables users of supercomputers or networked PCs to ...
The course offers basics of analyzing data with machine learning and data mining algorithms in order...
<p>Large scale machine learning has many characteristics that can be exploited in the system designs...
As ML applications are becoming ever more pervasive, fully-trained systems are made increasingly ava...
As Machine Learning (ML) applications are becoming ever more pervasive, fully-trained systems are ma...
Introduction We describe a novel architecture for a "linear algebra server" that operates...
Designing and implementing efficient, provably correct parallel machine learning (ML) algo-rithms is...
ABSTRACTThe rise of big data has led to new demands for machine learning (ML) systems to learn compl...
The rise of big data has led to new demands for machine learning (ML) systems to learn complex model...
: Machine learning using large data sets is a computationally intensive process. One technique that ...
How can one build a distributed framework that allows ef-ficient deployment of a wide spectrum of mo...
† These authors contributed equally. Machine learning (ML) and statistical techniques are key to tra...
MATLAB [7] is one of the most widely used mathematical computing environments in technical computing...
SystemML aims at declarative, large-scale machine learning (ML) on top of MapReduce, where high-leve...
When building large-scale machine learning (ML) programs, such as big topic models or deep neural ne...