15 citations · 23 across the 7 of their papers we have counts for
7 papers
Virtual Screening on FPGA: Performance and Energy versus Effort
Tom Vander Aa, Tom Haber, Thomas J. Ashby +2
With their widespread availability, FPGA-based accelerators cards have become an alternative to GPUs and CPUs to accelerate computing in applications with certain requirements (lik…
A High-Performance Implementation of Bayesian Matrix Factorization with Limited Communication
Tom Vander Aa, Xiangju Qin, Paul Blomstedt +3
Matrix factorization is a very common machine learning technique in recommender systems. Bayesian Matrix Factorization (BMF) algorithms would be attractive because of their ability…
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…