arXiv Machine Learning July 31, 2026 By Andrew Polar, Michael Poluektov

Concurrent training methods for Kolmogorov-Arnold networks: Disjoint datasets and FPGA implementation

Read the original on arXiv Machine Learning →

arXiv:2512. 18921v5 Announce Type: replace Abstract: The present paper introduces concurrency-driven enhancements to the training algorithm for the Kolmogorov-Arnold networks (KANs) that is based on the Newton-Kaczmarz (NK) method.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 18

KANEL\'E: Kolmogorov-Arnold Networks for Efficient LUT-based Evaluation

arXiv:2512. 12850v3 Announce Type: replace-cross Abstract: Low-latency, resource-efficient neural network inference on FPGAs is essential for applications demanding real-time capability and low power.

By Duc Hoang, Aarush Gupta, Philip Harris

efficiency benchmarks

arXiv Machine Learning

Jun 8

GS-KAN: Parameter-Efficient Kolmogorov-Arnold Networks via Sprecher-Type Shared Basis Functions

arXiv:2512. 09084v3 Announce Type: replace Abstract: The Kolmogorov-Arnold representation theorem offers a theoretical alternative to Multi-Layer Perceptrons (MLPs) by placing learnable univariate functions on edges rather than nodes.

By Oscar Eliasson

computer-vision

arXiv Machine Learning

Jun 24

Hardware-Oriented Inference Complexity of Kolmogorov-Arnold Networks

arXiv:2604. 03345v2 Announce Type: replace Abstract: Kolmogorov-Arnold Networks (KANs) have recently emerged as a powerful architecture for various machine learning applications.

By Bilal Khalid, Pedro Freire, Sergei K. Turitsyn, Jaroslaw E. Prilepsky

arXiv Machine Learning

Jun 18

Ultrafast On-chip Online Learning via Spline Locality in Kolmogorov-Arnold Networks

arXiv:2602. 02056v3 Announce Type: replace-cross Abstract: Ultrafast online learning is essential for high-frequency systems, such as controls for quantum computing and nuclear fusion, where adaptation must occur on sub-microsecond timescales.

By Duc Hoang, Aarush Gupta, Philip Harris

efficiency

arXiv AI

Jun 17

KANLib -- An Modular, Extensible and Fast Kolmogorov-Arnold Network Implementation

arXiv:2606. 17927v1 Announce Type: cross Abstract: Kolmogorov-Arnold Networks (KANs) have recently emerged as a promising alternative to traditional multilayer perceptrons by replacing linear weights with learnable univariate functions.

By Julian Hoever, Gregor Schiele

benchmarks safety

arXiv AI

Jun 24

KANLib -- A Modular, Extensible and Fast Kolmogorov-Arnold Network Implementation

arXiv:2606. 17927v2 Announce Type: replace-cross Abstract: Kolmogorov-Arnold Networks (KANs) have recently emerged as a promising alternative to traditional multilayer perceptrons by replacing linear weights with learnable univariate functions.

By Julian Hoever, Gregor Schiele

benchmarks safety

Related stories

KANEL\'E: Kolmogorov-Arnold Networks for Efficient LUT-based Evaluation

GS-KAN: Parameter-Efficient Kolmogorov-Arnold Networks via Sprecher-Type Shared Basis Functions

Hardware-Oriented Inference Complexity of Kolmogorov-Arnold Networks

Ultrafast On-chip Online Learning via Spline Locality in Kolmogorov-Arnold Networks

KANLib -- An Modular, Extensible and Fast Kolmogorov-Arnold Network Implementation

KANLib -- A Modular, Extensible and Fast Kolmogorov-Arnold Network Implementation