Kim Batselier | DeepAI

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∙ 09/11/2023

Quantized Fourier and Polynomial Features for more Expressive Tensor Network Models

In the context of kernel machines, polynomial and Fourier features are c...

0 Frederiek Wesel, et al. ∙

∙ 05/09/2023

How Informative is the Approximation Error from Tensor Decomposition for Neural Network Compression?

Tensor decompositions have been successfully applied to compress neural ...

0 Jetze T. Schuurmans, et al. ∙

∙ 05/25/2022

Towards Green AI with tensor networks – Sustainability and innovation enabled by efficient algorithms

The current standard to compare the performance of AI algorithms is main...

0 Eva Memmel, et al. ∙

∙ 10/26/2021

Tensor Network Kalman Filtering for Large-Scale LS-SVMs

Least squares support vector machines are a commonly used supervised lea...

0 Maximilian Lucassen, et al. ∙

∙ 09/03/2021

Large-Scale Learning with Fourier Features and Tensor Decompositions

Random Fourier features provide a way to tackle large-scale machine lear...

0 Frederiek Wesel, et al. ∙

∙ 12/21/2020

Alternating linear scheme in a Bayesian framework for low-rank tensor approximation

Multiway data often naturally occurs in a tensorial format which can be ...

0 Clara Menzen, et al. ∙

∙ 03/17/2020

Nonlinear system identification with regularized Tensor Network B-splines

This article introduces the Tensor Network B-spline model for the regula...

0 Ridvan Karagoz, et al. ∙

∙ 01/02/2020

Kernelized Support Tensor Train Machines

Tensor, a multi-dimensional data structure, has been exploited recently ...

15 Cong Chen, et al. ∙

∙ 12/20/2019

MERACLE: Constructive layer-wise conversion of a Tensor Train into a MERA

In this article two new algorithms are presented that convert a given da...

0 Kim Batselier, et al. ∙

∙ 08/07/2019

Faster Tensor Train Decomposition for Sparse Data

In recent years, the application of tensors has become more widespread i...

0 Lingjie Li, et al. ∙

∙ 11/12/2018

Matrix Product Operator Restricted Boltzmann Machines

A restricted Boltzmann machine (RBM) learns a probability distribution o...

22 Cong Chen, et al. ∙

∙ 11/09/2018

Deep Compression of Sum-Product Networks on Tensor Networks

Sum-product networks (SPNs) represent an emerging class of neural networ...

0 Ching-Yun Ko, et al. ∙

∙ 11/09/2018

The trouble with tensor ring decompositions

The tensor train decomposition decomposes a tensor into a "train" of 3-w...

0 Kim Batselier, et al. ∙

∙ 04/17/2018

Fast and Accurate Tensor Completion with Total Variation Regularized Tensor Trains

We propose a new tensor completion method based on tensor trains. The to...

0 Ching-Yun Ko, et al. ∙

∙ 04/17/2018

Fast and Accurate Tensor Completion with Tensor Trains: A System Identification Approach

We propose a novel tensor completion approach by equating it to a system...

0 Ching-Yun Ko, et al. ∙

∙ 04/17/2018

A Support Tensor Train Machine

There has been growing interest in extending traditional vector-based ma...

0 Cong Chen, et al. ∙

∙ 07/25/2017

Computing low-rank approximations of large-scale matrices with the Tensor Network randomized SVD

We propose a new algorithm for the computation of a singular value decom...

0 Kim Batselier, et al. ∙

∙ 12/20/2016

Parallelized Tensor Train Learning of Polynomial Classifiers

In pattern classification, polynomial classifiers are well-studied metho...

0 Zhongming Chen, et al. ∙

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