3 citations · 4 across the 3 of their papers we have counts for
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math.NA2019★ 1 cited
MERACLE: Constructive layer-wise conversion of a Tensor Train into a MERA
Kim Batselier, Andrzej Cichocki, Ngai Wong
In this article two new algorithms are presented that convert a given data tensor train into either a Tucker decomposition with orthogonal matrix factors or a multi-scale entanglem…
math.NA2019
Faster Tensor Train Decomposition for Sparse Data
Lingjie Li, Wenjian Yu, Kim Batselier
In recent years, the application of tensors has become more widespread in fields that involve data analytics and numerical computation. Due to the explosive growth of data, low-ran…
math.NA2017★ 3 cited
Computing low-rank approximations of large-scale matrices with the Tensor Network randomized SVD
Kim Batselier, Wenjian Yu, Luca Daniel +1
We propose a new algorithm for the computation of a singular value decomposition (SVD) low-rank approximation of a matrix in the Matrix Product Operator (MPO) format, also called t…