Researchers and data scientists are exploring the capabilities of Kolmogorov-Arnold Networks (KAN) in various applications, such as MRI scan data analysis and graph learning. The innovative architecture of KAN involves learnable non-linear activations in each pixel, automatic pruning for efficiency, and moving activation functions from nodes to edges. KAN is being compared favorably to Multi-Layer Perceptrons (MLPs) for approximating nonlinear functions.
Revisiting the by now famous Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs) for approximating nonlinear functions 👨🔧. 📌 Moving activation functions from nodes (neurons) to edges (weights)! 📌 MLPs place activation functions on… https://t.co/dqv1PketAg
Automatic pruning with Kolmogorov-Arnold Networks (KANs) to make the network sparser, more efficient, and more interpretable is one of the key contribution of the KAN algo. 👨🔧 The automatic pruning process in KANs works as follows: - For each node, the maximum L1 norm of its… https://t.co/VBolPnVsSj
KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning. https://t.co/6GHMDVMMWA
"In this article, we first explore splines, as they help us understand the architecture and key elements of KAN. Then, we make a deep dive inside the inner workings of KAN." Marco Peixeiro offers a hands-on introduction to Kolmogorov-Arnold Netrworks. https://t.co/RHZdeP2CqQ
Kolmogorov-Arnold network for MRI scan data analysis. #KAN consistently outperformed MLP. “CEST-KAN: Kolmogorov-Arnold Networks for CEST MRI Data Analysis” https://t.co/ctFZbzBVIK
This project extends the idea of the innovative architecture of Kolmogorov-Arnold Networks (KAN) to the Convolutional Layers, changing the classic linear transformation of the convolution to learnable non linear activations in each pixel. https://t.co/n9rg6iRiZ4 https://t.co/YEfIF4cq7p
We just published an article about KANs on Medium. -- Comparing Kolmogorov-Arnold Network(KAN ) and Multi-Layer Perceptrons (MLPs) https://t.co/nJ6EkzJtnm Hope its useful!