In the context of kernel machines, polynomial and Fourier features are
c...
Tensor decompositions have been successfully applied to compress neural
...
The current standard to compare the performance of AI algorithms is main...
Least squares support vector machines are a commonly used supervised lea...
Random Fourier features provide a way to tackle large-scale machine lear...
Multiway data often naturally occurs in a tensorial format which can be
...
This article introduces the Tensor Network B-spline model for the regula...
Tensor, a multi-dimensional data structure, has been exploited recently ...
In this article two new algorithms are presented that convert a given da...
In recent years, the application of tensors has become more widespread i...
A restricted Boltzmann machine (RBM) learns a probability distribution o...
Sum-product networks (SPNs) represent an emerging class of neural networ...
The tensor train decomposition decomposes a tensor into a "train" of 3-w...
We propose a new tensor completion method based on tensor trains. The
to...
We propose a novel tensor completion approach by equating it to a system...
There has been growing interest in extending traditional vector-based ma...
We propose a new algorithm for the computation of a singular value
decom...
In pattern classification, polynomial classifiers are well-studied metho...