Tensaurus: A versatile accelerator for mixed sparse-dense tensor computations
Tensor factorizations are powerful tools in many machine learning and data analytics
applications. Tensors are often sparse, which makes sparse tensor factorizations memory …
applications. Tensors are often sparse, which makes sparse tensor factorizations memory …
TuckerMPI: A parallel C++/MPI software package for large-scale data compression via the Tucker tensor decomposition
Our goal is compression of massive-scale grid-structured data, such as the multi-terabyte
output of a high-fidelity computational simulation. For such data sets, we have developed a …
output of a high-fidelity computational simulation. For such data sets, we have developed a …
Learning nonnegative factors from tensor data: Probabilistic modeling and inference algorithm
Tensor canonical polyadic decomposition (CPD) with nonnegative factor matrices, which
extracts useful latent information from multidimensional data, has found wide-spread …
extracts useful latent information from multidimensional data, has found wide-spread …
Toward decoding the relationship between domain structure and functionality in ferroelectrics via hidden latent variables
Polarization switching mechanisms in ferroelectric materials are fundamentally linked to
local domain structure and the presence of the structural defects, which both can act as …
local domain structure and the presence of the structural defects, which both can act as …
Comparison of accuracy and scalability of gauss--Newton and alternating least squares for CANDECOMC/PARAFAC decomposition
Alternating least squares is the most widely used algorithm for CANDECOMC/PARAFAC
(CP) tensor decomposition. However, alternating least squares may exhibit slow or no …
(CP) tensor decomposition. However, alternating least squares may exhibit slow or no …
Accelerating alternating least squares for tensor decomposition by pairwise perturbation
The alternating least squares (ALS) algorithm for CP and Tucker decomposition is
dominated in cost by the tensor contractions necessary to set up the quadratic optimization …
dominated in cost by the tensor contractions necessary to set up the quadratic optimization …
Efficient parallel CP decomposition with pairwise perturbation and multi-sweep dimension tree
The widely used alternating least squares (ALS) algorithm for the canonical polyadic (CP)
tensor decomposition is dominated in cost by the matricized-tensor times Khatri-Rao product …
tensor decomposition is dominated in cost by the matricized-tensor times Khatri-Rao product …
PLANC: Parallel low-rank approximation with nonnegativity constraints
We consider the problem of low-rank approximation of massive dense nonnegative tensor
data, for example, to discover latent patterns in video and imaging applications. As the size …
data, for example, to discover latent patterns in video and imaging applications. As the size …
Alternating Mahalanobis Distance Minimization for Accurate and Well-Conditioned CP Decomposition
Canonical polyadic decomposition (CPD) is prevalent in chemometrics, signal processing,
data mining, and many more fields. While many algorithms have been proposed to compute …
data mining, and many more fields. While many algorithms have been proposed to compute …
General memory-independent lower bound for MTTKRP
Our goal is to establish lower bounds on the communication required to perform the
Matricized-Tensor Times Khatri-Rao Product (MTTKRP) computation on a distributed …
Matricized-Tensor Times Khatri-Rao Product (MTTKRP) computation on a distributed …