47 citations · 105 across the 21 of their papers we have counts for
5 papers · 1 filter
Quantized Fourier and Polynomial Features for more Expressive Tensor Network Models
Frederiek Wesel, Kim Batselier
In the context of kernel machines, polynomial and Fourier features are commonly used to provide a nonlinear extension to linear models by mapping the data to a higher-dimensional s…
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…
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…
A Support Tensor Train Machine
Cong Chen, Kim Batselier, Ching-Yun Ko +1
There has been growing interest in extending traditional vector-based machine learning techniques to their tensor forms. An example is the support tensor machine (STM) that utilize…
Parallelized Tensor Train Learning of Polynomial Classifiers
Zhongming Chen, Kim Batselier, Johan A. K. Suykens +1
In pattern classification, polynomial classifiers are well-studied methods as they are capable of generating complex decision surfaces. Unfortunately, the use of multivariate polyn…