4 citations · 7 across the 16 of their papers we have counts for
8 papers · 1 filter
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…
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…
How Informative is the Approximation Error from Tensor Decomposition for Neural Network Compression?
Jetze T. Schuurmans, Kim Batselier, Julian F. P. Kooij
Tensor decompositions have been successfully applied to compress neural networks. The compression algorithms using tensor decompositions commonly minimize the approximation error o…
Position: Tensor Networks are a Valuable Asset for Green AI
Eva Memmel, Clara Menzen, Jetze Schuurmans +2
For the first time, this position paper introduces a fundamental link between tensor networks (TNs) and Green AI, highlighting their synergistic potential to enhance both the inclu…
Tensor Network Kalman Filtering for Large-Scale LS-SVMs
Maximilian Lucassen, Johan A. K. Suykens, Kim Batselier
Least squares support vector machines are a commonly used supervised learning method for nonlinear regression and classification. They can be implemented in either their primal or…
Alternating linear scheme in a Bayesian framework for low-rank tensor approximation
Clara Menzen, Manon Kok, Kim Batselier
Multiway data often naturally occurs in a tensorial format which can be approximately represented by a low-rank tensor decomposition. This is useful because complexity can be signi…
Kernelized Support Tensor Train Machines
Cong Chen, Kim Batselier, Wenjian Yu +1
Tensor, a multi-dimensional data structure, has been exploited recently in the machine learning community. Traditional machine learning approaches are vector- or matrix-based, and…