Tag
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.