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Highly Cited

2015

Highly Cited

2015

We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive…

Highly Cited

2011

Highly Cited

2011

We describe the University of Florida Sparse Matrix Collection, a large and actively growing set of sparse matrices that arise in…

Highly Cited

2011

Highly Cited

2011

Suppose we are given a matrix that is formed by adding an unknown sparse matrix to an unknown low-rank matrix. Our goal is to…

Highly Cited

2010

Highly Cited

2010

Sparse coding--that is, modelling data vectors as sparse linear combinations of basis elements--is widely used in machine…

Highly Cited

2009

Highly Cited

2009

Sparse matrix-vector multiplication (SpMV) is of singular importance in sparse linear algebra. In contrast to the uniform…

Highly Cited

2008

Highly Cited

2008

The massive parallelism of graphics processing units (GPUs) oers tremendous performance in many high-performance computing…

Highly Cited

2007

Highly Cited

2007

We are witnessing a dramatic change in computer architecture due to the multicore paradigm shift, as every electronic device from…

Review

2005

Review

2005

The Optimized Sparse Kernel Interface (OSKI) is a collection of low-level primitives that provide automatically tuned…

Highly Cited

2004

Highly Cited

2004

Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non…

Highly Cited

1999

Highly Cited

1999

A class of matrices (H-matrices) is introduced which have the following properties. (i) They are sparse in the sense that only…