15 citations · 21 across the 4 of their papers we have counts for
6 papers
Guidelines for enhancing data locality in selected machine learning algorithms
Imen Chakroun, Tom Vander Aa, Thomas J. Ashby
To deal with the complexity of the new bigger and more complex generation of data, machine learning (ML) techniques are probably the first and foremost used. For ML algorithms to p…
Reviewing Data Access Patterns and Computational Redundancy for Machine Learning Algorithms
Imen Chakroun, Tom Vander Aa, Tom Ashby
Machine learning (ML) is probably the first and foremost used technique to deal with the size and complexity of the new generation of data. In this paper, we analyze one of the mea…
SMURFF: a High-Performance Framework for Matrix Factorization
Tom Vander Aa, Imen Chakroun, Thomas J. Ashby +10
Bayesian Matrix Factorization (BMF) is a powerful technique for recommender systems because it produces good results and is relatively robust against overfitting. Yet BMF is more c…
Distributed Matrix Factorization using Asynchrounous Communication
Tom Vander Aa, Imen Chakroun, Tom Haber
Using the matrix factorization technique in machine learning is very common mainly in areas like recommender systems. Despite its high prediction accuracy and its ability to avoid…
Distributed Bayesian Probabilistic Matrix Factorization
Tom Vander Aa, Imen Chakroun, Tom Haber
Matrix factorization is a common machine learning technique for recommender systems. Despite its high prediction accuracy, the Bayesian Probabilistic Matrix Factorization algorithm…
A GPU-accelerated Branch-and-Bound Algorithm for the Flow-Shop Scheduling Problem
Melab Nouredine, Imen Chakroun, Mezmaz Mohand +1
Branch-and-Bound (B&B) algorithms are time intensive tree-based exploration methods for solving to optimality combinatorial optimization problems. In this paper, we investigate the…