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Related Experiment Video

Updated: Sep 13, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

LeanKAN: a parameter-lean Kolmogorov-Arnold network layer with improved memory efficiency and convergence behavior.

Benjamin C Koenig¹, Suyong Kim¹, Sili Deng¹

¹Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, MA, USA.

Neural Networks : the Official Journal of the International Neural Network Society

|July 30, 2025

Summary

LeanKANs offer a simpler, more efficient alternative to MultKAN layers in Kolmogorov-Arnold networks (KANs). These new layers improve KANs

Keywords:

Data-driven modeling Interpretable networks Kolmogorov-Arnold networks Machine learning Model discovery

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Last Updated: Sep 13, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Machine Learning
Artificial Intelligence
Deep Learning

Background:

Kolmogorov-Arnold Networks (KANs) present a novel architecture as an alternative to Multi-Layer Perceptrons (MLPs) for data-driven modeling.
The MultKAN layer was introduced to enhance KANs by incorporating both addition and multiplication subnodes, aiming for improved representational power.

Purpose of the Study:

To identify and address the limitations of MultKAN layers, specifically their restricted use in output layers, complex parameterization, and numerous hyperparameters.
To introduce LeanKANs as a direct, modular, and improved replacement for both MultKAN and traditional AddKAN layers.

Main Methods:

Proposed LeanKANs as a layer replacement, designed for general applicability in output layers, reduced parameter counts, and simplified hyperparameter sets.
Evaluated LeanKANs through direct layer replacement in standard KAN tasks and augmented structures like KAN Ordinary Differential Equations (KAN-ODEs) and Deep Operator KANs (DeepOKANs).

Main Results:

LeanKANs demonstrate superior performance compared to MultKANs, even when the latter have larger parameter counts, across various tasks including differential equations.
The sparser parameterization and compact structure of LeanKANs enhance their expressivity and learning capabilities.

Conclusions:

LeanKANs provide a more efficient and effective alternative to MultKAN and AddKAN layers, offering significant advantages in parameter efficiency and performance.
LeanKANs are versatile, serving as a backbone for advanced KAN architectures and improving learning outcomes in complex problems like KAN-ODEs.