- [DropKAN: Regularizing KANs by masking post-activations](https://arxiv.org/abs/2407.13044) : DropKAN (Dropout Kolmogorov-Arnold Networks) is a regularization method that prevents co-adaptation of activation function weights in Kolmogorov-Arnold Networks (KANs). DropKAN operates by randomly masking some of the post-activations within the KANs computation graph, while scaling-up the retained post-activations. We show that this simple procedure that require minimal coding effort has a regularizing effect and consistently lead to better generalization of KANs. | [code](https://github.com/Ghaith81/dropkan) ｜ 

- [Rethinking the Function of Neurons in KANs](https://arxiv.org/abs/2407.20667) : The neurons of Kolmogorov-Arnold Networks (KANs) perform a simple summation motivated by the Kolmogorov-Arnold representation theorem, Our findings indicate that substituting the sum with the average function in KAN neurons results in significant performance enhancements compared to traditional KANs. Our study demonstrates that this minor modification contributes to the stability of training by confining the input to the spline within the effective range of the activation function. | [code](https://github.com/Ghaith81/dropkan) ｜ 

- [CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival Analysis](https://arxiv.org/abs/2409.04290) : CoxKAN is a novel framework for survival analysis based on Kolmogorov-Arnold Networks, which combines both interpretability and high performance. CoxKAN outperforms traditional models like the Cox proportional hazards model and rivals deep learning-based models, but with the advantage of interpretability, making it more useful in medical settings where understanding the underlying risk factors and relationships is essential. We find that CoxKAN extracts complex interactions between predictor variables and identifies the precise effect of important biomarkers on patient survival. | [code](https://github.com/knottwill/coxkan) ｜ 

- [Effective Integration of KAN for Keyword Spotting](https://arxiv.org/abs/2409.08605)

- [Chebyshev Polynomial-Based Kolmogorov-Arnold Networks](https://arxiv.org/html/2405.07200v1)

- [Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving PDEs based on Kolmogorov Arnold Networks](https://arxiv.org/abs/2406.11045) | [code](https://github.com/yizheng-wang/research-on-solving-partial-differential-equations-of-solid-mechanics-based-on-pinn) ｜