arXiv Machine Learning August 10, 2026 By Albert Saiapin, Kim Batselier

Tensor Network Kernel Machines: A JAX Framework for Machine Learning and Nonlinear System Identification

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arXiv:2608. 07043v1 Announce Type: cross Abstract: Developing nonlinear models that are both expressive and computationally efficient remains a challenge in machine learning and nonlinear system identification.

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benchmarks

arXiv Machine Learning

Jun 10

Hyperparameter Learning for Latent Factorization of Tensors for Representation Learning to Large-scale Dynamic Weighted Directed Network

arXiv:2606. 09880v1 Announce Type: new Abstract: Large-scale dynamic weighted directed networks (DWDNs) are widely used to model time-varying interactions among nodes.

By Yaqian Zhan, Jialan He, Tianzhu Chen

rag

arXiv AI

Aug 19

Iterative tensor network transformations for element-wise evaluation of elementary and filtering functions

arXiv:2608. 17135v1 Announce Type: cross Abstract: Tensor networks are powerful formats for compressing large-scale data.

By Xiao Wang, Tomohiro Hashizume, Pia Siegl, Dieter Jaksch

arXiv Statistics ML

Sep 10

Tensor-Train Weak SINDy: Identifying High-Dimensional Nonlinear Dynamics

arXiv:2609.09434v1 Announce Type: cross Abstract: In recent years, weak-form methods have made significant advances in data-driven discovery of dynamical systems. However, in high-dimensional setting...

By Will Houser, Vanja Dukic, David M. Bortz

arXiv AI

Jul 2

K-Inverse-RFM: A Modified RFM that Bridges the Gap to Neural Networks for Data-Corrupted Mathematical Tasks

arXiv:2607. 00329v1 Announce Type: cross Abstract: Recursive Feature Machines (RFMs) are a class of kernel machines that utilize the Average Gradient Outer Product (AGOP) as a mechanism for feature learning.

By Gil Pasternak

arXiv AI

Jul 7

Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) for Exponential Parameter Compression of Deep Neural Networks

arXiv:2606. 00130v2 Announce Type: replace-cross Abstract: Large deep neural networks are costly to store and deploy because inference must move and evaluate many parameters.

By Andrzej Cichocki, Michal Wietczak

efficiency

arXiv Machine Learning

Sep 23

Riemannian Optimization on Tree Tensor Networks with Application in Machine Learning

The paper presents a formal analysis of the quotient geometry of tree tensor networks (TTNs) and introduces efficient first- and second-order optimization algorithms that leverage this geometry. It also develops a backpropagation method for training TTNs in a kernel learning context. Numerical experiments on a digit classification task demonstrate a tradeoff between two horizontal distributions: one provides clearer geometric insights, while the other yields more efficient algorithms.

By Marius Willner, Marco Trenti, Dirk Lebiedz

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