3 citations · 4 across the 3 of their papers we have counts for
6 papers
Large-Scale Learning with Fourier Features and Tensor Decompositions
Frederiek Wesel, Kim Batselier
Random Fourier features provide a way to tackle large-scale machine learning problems with kernel methods. Their slow Monte Carlo convergence rate has motivated the research of det…
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…
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…
Matrix Product Operator Restricted Boltzmann Machines
Cong Chen, Kim Batselier, Ching-Yun Ko +1
A restricted Boltzmann machine (RBM) learns a probability distribution over its input samples and has numerous uses like dimensionality reduction, classification and generative mod…
Deep Compression of Sum-Product Networks on Tensor Networks
Ching-Yun Ko, Cong Chen, Yuke Zhang +2
Sum-product networks (SPNs) represent an emerging class of neural networks with clear probabilistic semantics and superior inference speed over graphical models. This work reveals…
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…