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Found 5 papers, 1 papers with code
A High-Performance Implementation of Bayesian Matrix Factorization with Limited Communication
We show that the combination of the two methods gives substantial improvements in the scalability of BMF on web-scale datasets, when the goal is to reduce the wall-clock time.
Guidelines for enhancing data locality in selected machine learning algorithms
In this paper, we analyze one of the means to increase the performances of machine learning algorithms which is exploiting data locality.
Reviewing Data Access Patterns and Computational Redundancy for Machine Learning Algorithms
Altering the access patterns to increase locality can dramatically increase performance of a given algorithm.
SMURFF: a High-Performance Framework for Matrix Factorization
Bayesian Matrix Factorization (BMF) is a powerful technique for recommender systems because it produces good results and is relatively robust against overfitting.
Distributed Matrix Factorization using Asynchrounous Communication
Using the matrix factorization technique in machine learning is very common mainly in areas like recommender systems.
Distributed, Parallel, and Cluster Computing
