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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。
The paper derives a uniform concentration bound for two-timescale actor-critic algorithms with function approximation in reinforcement learning, analyzing the actor parameter's behavior with high probability.
RecKAN introduces a learnable recursive polynomial basis for Kolmogorov-Arnold Networks, outperforming existing KAN variants on classification and forecasting tasks.
This paper proposes Fourier Feature Networks (FENs), a single-hidden-layer neural network architecture using Fourier features to solve partial differential equations, achieving higher accuracy than Extreme Learning Machines without affine transformations.
This paper proves that EML trees, which represent elementary functions through composition, are universal approximators for continuous functions and other functional spaces. The proof constructs EML representations of basic operations and uses them as building blocks.
We propose hierarchical RBF-KAN and RBF-SKAN architectures for multidimensional function approximation and random field learning. The frameworks offer universal approximation properties and partially alleviate the curse of dimensionality, with empirical results showing improved accuracy over existing methods.
This paper proposes behavior-aware auxiliary corrections for off-policy temporal-difference prediction, introducing BA-TDC and BA-TDRC algorithms that replace the auxiliary covariance matrix with the behavior Bellman matrix to improve stability and convergence. Theoretical analysis and experiments on standard benchmarks validate the effectiveness of the proposed methods.
This paper introduces Geometric Kolmogorov-Arnold Networks (GeoKAN), a family of geometry-aware models that learn Riemannian metrics to adapt coordinates for improved function approximation and physics-informed learning.