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
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
KAN versus MLP on Irregular or Noisy Functions
Chen Zeng, Jiahui Wang, Haoran Shen, Qiao Wang
VulCatch: Enhancing Binary Vulnerability Detection through CodeT5 Decompilation and KAN Advanced Feature Extraction
Abdulrahman Hamman Adama Chukkol, Senlin Luo, Kashif Sharif, Yunusa Haruna, Muhammad Muhammad Abdullahi
KAN You See It? KANs and Sentinel for Effective and Explainable Crop Field Segmentation
Daniele Rege Cambrin, Eleonora Poeta, Eliana Pastor, Tania Cerquitelli, Elena Baralis, Paolo Garza
Higher-order-ReLU-KANs (HRKANs) for solving physics-informed neural networks (PINNs) more accurately, robustly and faster
Chi Chiu So, Siu Pang Yung
Kolmogorov-Arnold Network for Online Reinforcement Learning
Victor Augusto Kich, Jair Augusto Bottega, Raul Steinmetz, Ricardo Bedin Grando, Ayano Yorozu, Akihisa Ohya