Hugging Face Trending Papers July 15, 2026

Is the Statistical Advantage Worth the Cost? An Empirical Comparison of KANs and MLPs for Structured Data Classification

Read the original on Hugging Face Trending Papers →

This study presents an empirical benchmarking comparison between Kolmogorov-Arnold Networks (KANs) and Multi-Layer Perceptrons (MLPs) on structured tabular classification tasks. Motivated by the growing interest in KANs as an alternative function-approximating architecture, we evaluate their out-of-the-box performance on twelve publicly available datasets spanning binary, multiclass, multilabel, and ordinal problems.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

benchmarks

arXiv AI

Jul 16

Is the Statistical Advantage Worth the Cost? An Empirical Comparison of KANs and MLPs for Structured Data Classification

arXiv:2607. 13413v1 Announce Type: cross Abstract: This study presents an empirical benchmarking comparison between Kolmogorov-Arnold Networks (KANs) and Multi-Layer Perceptrons (MLPs) on structured tabular classification tasks.

By Matthew Steven P. Toledo, Justine Raphael H. Jacinto, Vivekjeet Singh Chambal, Rodolfo C. Camaclang III, Jamlech Iram N. Gojo Cruz, Reginald Neil C. Recario

benchmarks

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

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 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 AI

Jul 20

Kolmogorov--Arnold Networks for Small Language Models

arXiv:2607. 15525v1 Announce Type: cross Abstract: Kolmogorov--Arnold Networks (KANs) replace fixed node activations with learned one-dimensional edge functions, offering an explicit interface for interpretation and a possible alternative to transformer feed-forward networks.

By Felippe Alves, Renato Vicente

llms efficiency benchmarks

Related stories

Is the Statistical Advantage Worth the Cost? An Empirical Comparison of KANs and MLPs for Structured Data Classification

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

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

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

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

Kolmogorov--Arnold Networks for Small Language Models