4 citations · 7 across the 16 of their papers we have counts for
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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…
Tensor network square root Kalman filter for online Gaussian process regression
Clara Menzen, Manon Kok, Kim Batselier
The state-of-the-art tensor network Kalman filter lifts the curse of dimensionality for high-dimensional recursive estimation problems. However, the required rounding operation can…
Constructing structured tensor priors for Bayesian inverse problems
Kim Batselier
Specifying a prior distribution is an essential part of solving Bayesian inverse problems. The prior encodes a belief on the nature of the solution and this regularizes the problem…
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…