1 citations · 1 across the 6 of their papers we have counts for
6 papers
A Kernelizable Primal-Dual Formulation of the Multilinear Singular Value Decomposition
Frederiek Wesel, Kim Batselier
The ability to express a learning task in terms of a primal and a dual optimization problem lies at the core of a plethora of machine learning methods. For example, Support Vector…
Exploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matrices
Frida Viset, Anton Kullberg, Frederiek Wesel +1
The Hilbert-space Gaussian Process (HGP) approach offers a hyperparameter-independent basis function approximation for speeding up Gaussian Process (GP) inference by projecting the…
Efficient Patient Fine-Tuned Seizure Detection with a Tensor Kernel Machine
Seline J. S. de Rooij, Frederiek Wesel, Borbála Hunyadi
Recent developments in wearable devices have made accurate and efficient seizure detection more important than ever. A challenge in seizure detection is that patient-specific model…
Tensor Network-Constrained Kernel Machines as Gaussian Processes
Frederiek Wesel, Kim Batselier
Tensor Networks (TNs) have recently been used to speed up kernel machines by constraining the model weights, yielding exponential computational and storage savings. In this paper w…
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…