Stochastic Gradients for Large-Scale Tensor Decomposition

T. G. Kolda and D. Hong, SIAM Journal on Mathematics of Data Science

Practical Leverage-Based Sampling for Low-Rank Tensor Decomposition

B. W. Larsen and T. G. Kolda, , 2020

Estimating Higher-Order Moments Using Symmetric Tensor Decomposition

S. Sherman and T. G. Kolda, SIAM Journal on Matrix Analysis and Applications, 2020

TuckerMPI: A Parallel C++/MPI Software Package for Large-scale Data Compression via the Tucker Tensor Decomposition

G. Ballard, A. Klinvex and T. G. Kolda, ACM Transactions on Mathematical Software, 2020

Generalized Canonical Polyadic Tensor Decomposition

D. Hong, T. G. Kolda and J. A. Duersch, SIAM Review, 2020

Faster Johnson-Lindenstrauss Transforms via Kronecker Products

R. Jin, T. G. Kolda and R. Ward, , 2019

Software for Sparse Tensor Decomposition on Emerging Computing Architectures

E. Phipps and T. G. Kolda, SIAM Journal on Scientific Computing, 2019

A Practical Randomized CP Tensor Decomposition

C. Battaglino, G. Ballard and T. G. Kolda, SIAM Journal on Matrix Analysis and Applications, 2018

Unsupervised Discovery of Demixed, Low-dimensional Neural Dynamics across Multiple Timescales through Tensor Components Analysis

A. H. Williams, T. H. Kim, F. Wang, S. Vyas, S. I. Ryu, K. V. Shenoy, M. Schnitzer, T. G. Kolda and S. Ganguli, Neuron, 2018

Triangular Alignment (TAME): A Tensor-based Approach for Higher-order Network Alignment

S. Mohammadi, D. F. Gleich, T. G. Kolda and A. Grama, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2017