Kan 2.0: Kolmogorov-arnold networks meet science
A major challenge of AI+ Science lies in their inherent incompatibility: today's AI is primarily
based on connectionism, while science depends on symbolism. To bridge the two worlds …
based on connectionism, while science depends on symbolism. To bridge the two worlds …
A survey on kolmogorov-arnold network
This systematic review explores the theoretical foundations, evolution, applications, and
future potential of Kolmogorov-Arnold Networks (KAN), a neural network model inspired by …
future potential of Kolmogorov-Arnold Networks (KAN), a neural network model inspired by …
On the expressiveness and spectral bias of KANs
Kolmogorov-Arnold Networks (KAN)\cite {liu2024kan} were very recently proposed as a
potential alternative to the prevalent architectural backbone of many deep learning models …
potential alternative to the prevalent architectural backbone of many deep learning models …
Dropkan: Regularizing kans by masking post-activations
MG Altarabichi - arxiv preprint arxiv:2407.13044, 2024 - arxiv.org
We propose DropKAN (Dropout Kolmogorov-Arnold Networks) a regularization method that
prevents co-adaptation of activation function weights in Kolmogorov-Arnold Networks …
prevents co-adaptation of activation function weights in Kolmogorov-Arnold Networks …
Kolmogorov-Arnold PointNet: Deep learning for prediction of fluid fields on irregular geometries
A Kashefi - arxiv preprint arxiv:2408.02950, 2024 - arxiv.org
We present Kolmogorov-Arnold PointNet (KA-PointNet) as a novel supervised deep learning
framework for the prediction of incompressible steady-state fluid flow fields in irregular …
framework for the prediction of incompressible steady-state fluid flow fields in irregular …
F-kans: Federated kolmogorov-arnold networks
In this paper, we present an innovative federated learning (FL) approach that utilizes
Kolmogorov-Arnold Networks (KANs) for classification tasks. By utilizing the adaptive …
Kolmogorov-Arnold Networks (KANs) for classification tasks. By utilizing the adaptive …
Lss-skan: Efficient kolmogorov-arnold networks based on single-parameterized function
The recently proposed Kolmogorov-Arnold Networks (KAN) networks have attracted
increasing attention due to their advantage of high visualizability compared to MLP. In this …
increasing attention due to their advantage of high visualizability compared to MLP. In this …
Rethinking the function of neurons in kans
MG Altarabichi - arxiv preprint arxiv:2407.20667, 2024 - arxiv.org
The neurons of Kolmogorov-Arnold Networks (KANs) perform a simple summation motivated
by the Kolmogorov-Arnold representation theorem, which asserts that sum is the only …
by the Kolmogorov-Arnold representation theorem, which asserts that sum is the only …
Generalization bounds and model complexity for kolmogorov-arnold networks
X Zhang, H Zhou - arxiv preprint arxiv:2410.08026, 2024 - arxiv.org
Kolmogorov-Arnold Network (KAN) is a network structure recently proposed by Liu et
al.(2024) that offers improved interpretability and a more parsimonious design in many …
al.(2024) that offers improved interpretability and a more parsimonious design in many …
PointNet with KAN versus PointNet with MLP for 3D Classification and Segmentation of Point Sets
A Kashefi - arxiv preprint arxiv:2410.10084, 2024 - arxiv.org
We introduce PointNet-KAN, a neural network for 3D point cloud classification and
segmentation tasks, built upon two key components. First, it employs Kolmogorov-Arnold …
segmentation tasks, built upon two key components. First, it employs Kolmogorov-Arnold …