4 citations · 7 across the 16 of their papers we have counts for
16 papers
Tensor Network Kernel Machines: A JAX Framework for Machine Learning and Nonlinear System Identification
Albert Saiapin, Kim Batselier
Developing nonlinear models that are both expressive and computationally efficient remains a challenge in machine learning and nonlinear system identification. Tensor network kerne…
Laplace Approximation for Bayesian Tensor Network Kernel Machines
Albert Saiapin, Kim Batselier
Uncertainty estimation is essential for robust decision-making in the presence of ambiguous or out-of-distribution inputs. Gaussian Processes (GPs) are classical kernel-based model…
A Fully Probabilistic Tensor Network for Regularized Volterra System Identification
Afra Kilic, Kim Batselier
Modeling nonlinear systems with Volterra series is challenging because the number of kernel coefficients grows exponentially with the model order. This work introduces Bayesian Ten…
Automatic Structure Identification for Highly Nonlinear MIMO Volterra Tensor Networks
Eva Memmel, Kim Batselier
The Volterra Tensor Network lifts the curse of dimensionality for truncated, discrete times Volterra models, enabling scalable representation of highly nonlinear system. This scala…
Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
Afra Kilic, Kim Batselier
Tensor Network (TN) Kernel Machines speed up model learning by representing parameters as low-rank TNs, reducing computation and memory use. However, most TN-based Kernel methods a…
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…
Projecting basis functions with tensor networks for Gaussian process regression
Clara Menzen, Eva Memmel, Kim Batselier +1
This paper presents a method for approximate Gaussian process (GP) regression with tensor networks (TNs). A parametric approximation of a GP uses a linear combination of basis func…
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…
Nonlinear system identification with regularized Tensor Network B-splines
Ridvan Karagoz, Kim Batselier
This article introduces the Tensor Network B-spline model for the regularized identification of nonlinear systems using a nonlinear autoregressive exogenous (NARX) approach. Tensor…
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…