kolmogorov-arnold-networks

该论文提出 SW-KAN，一种基于 Stieltjes-Wigert q-正交多项式的 Kolmogorov-Arnold 网络，通过指数-tanh 域映射和 O(N) 三递推计算，在图像分类与函数逼近任务中实现更优的精度-效率权衡。

FlashKAN proposes a method to accelerate Kolmogorov-Arnold Networks by replacing Cox-de Boor recursion with truncated power form for B-spline evaluation, providing a fused GPU implementation and an open-source package.

RecKAN introduces a learnable recursive polynomial basis for Kolmogorov-Arnold Networks, outperforming existing KAN variants on classification and forecasting tasks.

Introduces geometry-constrained Kolmogorov-Arnold Networks that learn edge geometry via Banach duality, demonstrating superior performance in symbolic regression tasks, especially under measurement noise.

ER-KAN is a new variant of Kolmogorov-Arnold Networks designed for data-scarce and noisy scientific machine learning, showing improved robustness and efficiency over existing KAN variants.

This paper introduces a hybrid quantum-inspired Kolmogorov-Arnold network for privacy-aware federated learning of ECG data, demonstrating reduced parameters and communication costs while improving classification metrics compared to traditional MLP.

SineKAN presents a variant of Kolmogorov-Arnold Networks using sinusoidal activation functions, showing comparable or better performance with significant speed improvements over baseline KAN models on benchmark tasks.

SparseKAN is a unified compression method for Kolmogorov–Arnold Networks that prunes basis functions, neurons, and numerical precision under learnable gates, achieving up to 73% parameter reduction and significant latency improvements on software and FPGA hardware.

This paper evaluates Kolmogorov–Arnold Networks (KANs) versus MLPs as residual branches in hard-constrained recurrent physics-informed networks on an embedded RISC-V platform, finding KANs run slower, consume more energy, and are less dependable under INT8 quantization.

This paper introduces Complementary Matrix Gating (CMG) for QKAN-based fast-weight programmers, enabling coordinate-wise memory control for quantum dynamics forecasting. The method shows consistent improvements and low mean-squared errors on quantum simulation benchmarks.

This paper proposes LFS-FRAME, a leakage-free stacked ensemble framework integrating Kolmogorov-Arnold Networks and XGBoost for robust multiclass classification, achieving 89.85% accuracy on major families and 81.74% on sub-families.

SechKAN is a novel Kolmogorov-Arnold Network architecture that uses hyperbolic secant functions as basis functions, achieving competitive performance in function fitting, PDE problems, and image classification tasks while maintaining parameter efficiency comparable to MLPs.

This paper evaluates Kolmogorov-Arnold Networks (KANs) as interpretable components and replacements for transformer feed-forward networks in small language models, finding that while KANs provide a practical audit interface, they show no consistent benchmark advantage over MLP baselines.

This study empirically compares Kolmogorov-Arnold Networks (KANs) and Multi-Layer Perceptrons (MLPs) on structured tabular classification tasks, finding that KANs statistically outperform MLPs but with higher computational cost.

This paper introduces STKAN, a spatio-temporal forecasting architecture that integrates Taylor-polynomial Kolmogorov-Arnold Network modules for spatial and temporal token mixing. Experiments on five traffic benchmarks show competitive performance, suggesting nonlinear function approximators can complement architectural design.

Proposes Geometry-aware R-Structured KAN (GRS-KAN), a hybrid neural architecture that integrates R-functions into KAN to encode geometric and logical constraints, achieving up to 67% RMSE reduction on regression benchmarks with discontinuities.

This paper presents low-power analogue neural networks that place trainable nonlinear functions on connections, inspired by Kolmogorov-Arnold networks, enabling efficient continuous control tasks with far fewer nodes and connections than multilayer perceptrons, demonstrated on hardware with projected microWatt power.

This post explains the author's Master's thesis on using Kolmogorov-Arnold Networks (KANs) for ultrafast machine learning on FPGAs, achieving sub-microsecond inference and online learning via custom hardware architectures. It references two accepted papers: KANELÉ for LUT-based evaluation (FPGA 2026 Best Paper) and a method for on-FPGA online learning (ICML 2026).

This paper systematically explores hybrid KAN and MLP architectures for IMU-based human activity recognition, achieving a 5.33% average macro F1 improvement over pure MLP baselines.

This paper establishes the first population risk bounds for Kolmogorov-Arnold Networks trained with mini-batch SGD and DP-SGD using correlated noise, advancing theoretical understanding of KANs in privacy-sensitive domains.