arXiv Machine Learning
KANLab: TensorFlow Playground for Kolmogorov-Arnold Networks
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arXiv AI
Aug 13
HYDRA: Hyperbolic Dynamic Representation Architecture for Kolmogorov-Arnold Networks
arXiv:2608. 12194v1 Announce Type: cross Abstract: Kolmogorov-Arnold Networks (KANs) enhance nonlinear function approximation by replacing scalar weights with learnable univariate functions.
arXiv Machine Learning
Oct 9
Sample-Efficiency of Kolmogorov-Arnold Networks
The paper investigates the sample‑efficiency of Kolmogorov‑Arnold Networks (KANs) in reinforcement learning. Using experiments on the Feynman dataset and Gymnasium benchmarks, the authors find that KANs achieve comparable performance to standard Multi‑Layer Perceptrons while requiring 40% fewer samples, with up to 50% relative performance gains during training. These improvements hold even when reward noise varies, underscoring KANs’ potential for more sample‑efficient RL.
arXiv AI
Aug 13
Making Gaussian Kolmogorov-Arnold Networks Reliable and Accurate
arXiv:2604. 21174v3 Announce Type: replace-cross Abstract: Kolmogorov-Arnold Networks (KANs) replace fixed activations with learnable univariate edge functions whose behavior depends strongly on the chosen basis.
arXiv AI
Jul 22
SechKAN: Kolmogorov-Arnold Networks with Hyperbolic Secant Functions
arXiv:2607. 18290v1 Announce Type: cross Abstract: In recent years, Kolmogorov-Arnold Networks (KANs) have attracted increasing attention due to their effectiveness in machine learning and scientific computing tasks, offering a new paradigm for neural network design.
arXiv Machine Learning
Sep 23
Evaluating the Effectiveness of SechKAN on 1D Data
arXiv:2609.25876v1 Announce Type: new Abstract: The connection between the Kolmogorov-Arnold representation theorem (KART) and neural network design has led to the development of Kolmogorov-Arnold Ne...
arXiv Machine Learning
Oct 16
Kolmogorov-Arnold Networks: Curse of Dimensionality and Structural Bias
The paper investigates Kolmogorov‑Arnold networks (KANs) and their approximation properties. It shows that while KANs can achieve dimension‑independent approximation exponents for targets with smooth univariate edge functions, this advantage is tied to the target structure rather than the architecture itself. Empirical tests reveal that KANs outperform MLPs only when each nonlinear component depends on a single input coordinate; otherwise, MLPs can be more efficient, especially under tight parameter budgets or for interacting targets.