Add to ORKGMachine learning is the study of computer algorithms that focuses on analyzing and interpreting patterns and structures in data. It has been successfully applied to many areas in computer science and achieved state-of-the-art results to enable learning, reasoning, and decision-making without human interactions. This research aims to develop innovated data parallel frameworks to accommodate the computing resources to parallelize different machine learning and deep learning algorithms and speed up the training. To achieve that, we explore three interesting frameworks in this dissertation: (1) Sync-on-the-fly framework for gradient descent algorithms on transient resources; (2) Asynchronous Proactive Data Parallel framework for both gradient d...
High-performance computing (HPC) and machine learning (ML) have been widely adopted by both academia...
Los algoritmos de Machine Learning se benefician de la gran cantidad de datos disponible. Cuanto may...
As deep learning techniques become more and more popular, there is the need to move these applicatio...
Deep learning has been a very popular topic in Artificial Intelligent industry these years and can b...
Implementing machine learning algorithms for large data, such as the Web graph and social networks, ...
Implementing machine learning algorithms for large data, such as the Web graph and social networks, ...
In recent years, machine learning (ML) and, more noticeably, deep learning (DL), have be- come incre...
There is an increased interest in building machine learning frameworks with advanced algebraic capab...
Deep neural network models can achieve greater performance in numerous machine learning tasks by rai...
The area of machine learning has made considerable progress over the past decade, enabled by the wid...
Implementing machine learning algorithms for large data, such as the Web graph and social networks, ...
In the last two decades deep learning has attracted a lot of attention internationally, solving prob...
Designing and implementing efficient, provably correct parallel machine learning (ML) algo-rithms is...
Abstract. Learning multiple levels of feature detectors in Deep Be-lief Networks is a promising appr...
Two concurrent implementations of the method of conjugate gradients for training Elman networks are ...
High-performance computing (HPC) and machine learning (ML) have been widely adopted by both academia...
Los algoritmos de Machine Learning se benefician de la gran cantidad de datos disponible. Cuanto may...
As deep learning techniques become more and more popular, there is the need to move these applicatio...
Deep learning has been a very popular topic in Artificial Intelligent industry these years and can b...
Implementing machine learning algorithms for large data, such as the Web graph and social networks, ...
Implementing machine learning algorithms for large data, such as the Web graph and social networks, ...
In recent years, machine learning (ML) and, more noticeably, deep learning (DL), have be- come incre...
There is an increased interest in building machine learning frameworks with advanced algebraic capab...
Deep neural network models can achieve greater performance in numerous machine learning tasks by rai...
The area of machine learning has made considerable progress over the past decade, enabled by the wid...
Implementing machine learning algorithms for large data, such as the Web graph and social networks, ...
In the last two decades deep learning has attracted a lot of attention internationally, solving prob...
Designing and implementing efficient, provably correct parallel machine learning (ML) algo-rithms is...
Abstract. Learning multiple levels of feature detectors in Deep Be-lief Networks is a promising appr...
Two concurrent implementations of the method of conjugate gradients for training Elman networks are ...
High-performance computing (HPC) and machine learning (ML) have been widely adopted by both academia...
Los algoritmos de Machine Learning se benefician de la gran cantidad de datos disponible. Cuanto may...
As deep learning techniques become more and more popular, there is the need to move these applicatio...