Add to ORKGWe present a novel parallelisation scheme that simplifies the adaptation of learning algorithms to growing amounts of data as well as growing needs for accurate and confident predictions in critical applications. In contrast to other parallelisation techniques, it can be applied to a broad class of learning algorithms without further mathematical derivations and without writing dedicated code, while at the same time maintaining theoretical performance guarantees. Moreover, our parallelisation scheme is able to reduce the runtime of many learning algorithms to polylogarithmic time on quasi-polynomially many processing units. This is a significant step towards a general answer to an open question [21] on efficient parallelisation of machine l...
Learning belief networks from large domains can be expensive even with single-link lookahead search ...
: Machine learning using large data sets is a computationally intensive process. One technique that ...
Parallelizing neural networks is an active area of research. Current approaches surround the paralle...
We present a novel parallelisation scheme that simplifies the adaptation of learning algorithms to g...
Many machine learning algorithms iteratively process datapoints and transform global model parameter...
We are at the beginning of the multicore era. Computers will have increasingly many cores (processor...
Much of the research in inductive learning concentrates on problems with relatively small amounts of...
AbstractThis paper outlines a theory of parallel algorithms that emphasizes two crucial aspects of p...
High-performance computing (HPC) and machine learning (ML) have been widely adopted by both academia...
Large scale machine learning requires tradeoffs. Commonly this tradeoff has led practitioners to cho...
Parallel and distributed algorithms have become a necessity in modern machine learning tasks. In th...
The ability to leverage large-scale hardware parallelism has been one of the key enablers of the acc...
The Support Vector Machine (SVM) is a supervised algorithm for the solution of classification and re...
Numerical simulations are ubiquitous in science and engineering. Machine learning for science invest...
The big-data is an oil of this century. A high amount of computational power is required to get know...
Learning belief networks from large domains can be expensive even with single-link lookahead search ...
: Machine learning using large data sets is a computationally intensive process. One technique that ...
Parallelizing neural networks is an active area of research. Current approaches surround the paralle...
We present a novel parallelisation scheme that simplifies the adaptation of learning algorithms to g...
Many machine learning algorithms iteratively process datapoints and transform global model parameter...
We are at the beginning of the multicore era. Computers will have increasingly many cores (processor...
Much of the research in inductive learning concentrates on problems with relatively small amounts of...
AbstractThis paper outlines a theory of parallel algorithms that emphasizes two crucial aspects of p...
High-performance computing (HPC) and machine learning (ML) have been widely adopted by both academia...
Large scale machine learning requires tradeoffs. Commonly this tradeoff has led practitioners to cho...
Parallel and distributed algorithms have become a necessity in modern machine learning tasks. In th...
The ability to leverage large-scale hardware parallelism has been one of the key enablers of the acc...
The Support Vector Machine (SVM) is a supervised algorithm for the solution of classification and re...
Numerical simulations are ubiquitous in science and engineering. Machine learning for science invest...
The big-data is an oil of this century. A high amount of computational power is required to get know...
Learning belief networks from large domains can be expensive even with single-link lookahead search ...
: Machine learning using large data sets is a computationally intensive process. One technique that ...
Parallelizing neural networks is an active area of research. Current approaches surround the paralle...