Add to ORKGCoded Computing presents a novel method of computing that uses coding theory to overcome major bottlenecks in large-scale machine learning and big data analytics computing systems
The rise of big data has led to new demands for machine learning (ML) systems to learn complex model...
Coding theory has become an essential ingredient of contemporary information technology, and it rema...
Fast, effective, and reliable models: these are the desiderata of every theorist and practitioner. M...
Modern data centers have been providing exponentially increasing computing and storage resources, wh...
Robustness is a fundamental and timeless issue, and it remains vital to all aspects of computation s...
As an increasing number of modern big data systems utilize horizontal scaling,the general trend in t...
Coded computation techniques provide robustness against straggling workers in distributed computing....
The current BigData era routinely requires the processing of large scale data on massive distributed...
A ubiquitous problem in computer science research is the optimization of computation on large data s...
Gradient descent (GD) methods are commonly employed in machine learning problems to optimize the par...
Data and analytics capabilities have made a leap forward in recent years. The volume of available da...
Coded distributed computing framework enables large-scale machine learning (ML) models to be trained...
This article aims to give a comprehensive and rigorous review of the principles and recent developme...
Coded computation techniques provide robustness against straggling workers in distributed computing....
This dissertation develops a method for integrating information theoretic principles in distributed ...
The rise of big data has led to new demands for machine learning (ML) systems to learn complex model...
Coding theory has become an essential ingredient of contemporary information technology, and it rema...
Fast, effective, and reliable models: these are the desiderata of every theorist and practitioner. M...
Modern data centers have been providing exponentially increasing computing and storage resources, wh...
Robustness is a fundamental and timeless issue, and it remains vital to all aspects of computation s...
As an increasing number of modern big data systems utilize horizontal scaling,the general trend in t...
Coded computation techniques provide robustness against straggling workers in distributed computing....
The current BigData era routinely requires the processing of large scale data on massive distributed...
A ubiquitous problem in computer science research is the optimization of computation on large data s...
Gradient descent (GD) methods are commonly employed in machine learning problems to optimize the par...
Data and analytics capabilities have made a leap forward in recent years. The volume of available da...
Coded distributed computing framework enables large-scale machine learning (ML) models to be trained...
This article aims to give a comprehensive and rigorous review of the principles and recent developme...
Coded computation techniques provide robustness against straggling workers in distributed computing....
This dissertation develops a method for integrating information theoretic principles in distributed ...
The rise of big data has led to new demands for machine learning (ML) systems to learn complex model...
Coding theory has become an essential ingredient of contemporary information technology, and it rema...
Fast, effective, and reliable models: these are the desiderata of every theorist and practitioner. M...