Kolmogorov Arnold Network
Kolmogorov-Arnold Networks (KANs) are a novel type of neural network architecture that uses learnable activation functions placed on edges, rather than nodes, offering a potential alternative to traditional Multi-Layer Perceptrons (MLPs). Current research focuses on improving KAN efficiency and accuracy through variations like SincKANs and EKANs (Equivariant KANs), exploring their application in diverse fields such as image processing, function approximation, and solving partial differential equations. The significance of KANs lies in their potential for enhanced interpretability and performance in specific tasks, although comparisons with MLPs reveal varying degrees of success depending on the application and dataset characteristics.
Papers
DualKanbaFormer: Kolmogorov-Arnold Networks and State Space Model Transformer for Multimodal Aspect-based Sentiment Analysis
Adamu Lawan, Juhua Pu, Haruna Yunusa, Muhammad Lawan, Aliyu Umar, Adamu Sani Yahya
GINN-KAN: Interpretability pipelining with applications in Physics Informed Neural Networks
Nisal Ranasinghe, Yu Xia, Sachith Seneviratne, Saman Halgamuge
Kolmogorov Arnold Networks in Fraud Detection: Bridging the Gap Between Theory and Practice
Yang Lu, Felix Zhan
The Dawn of KAN in Image-to-Image (I2I) Translation: Integrating Kolmogorov-Arnold Networks with GANs for Unpaired I2I Translation
Arpan Mahara, Naphtali D. Rishe, Liangdong Deng
Activation Space Selectable Kolmogorov-Arnold Networks
Zhuoqin Yang, Jiansong Zhang, Xiaoling Luo, Zheng Lu, Linlin Shen