•1 min read•from Machine Learning
[R] SineKAN: Kolmogorov-Arnold Networks Using Sinusoidal Activation Functions
![[R] SineKAN: Kolmogorov-Arnold Networks Using Sinusoidal Activation Functions](/_next/image?url=https%3A%2F%2Fexternal-preview.redd.it%2Fq3evP6JeDpAC2MdSQHWYxnCYTqbJkElIQsLFqVSdkss.png%3Fwidth%3D640%26crop%3Dsmart%26auto%3Dwebp%26s%3Dde730fbf7ecace6df0036b21470c16a2d4feacfb&w=3840&q=75)
| I couldn't sleep because I couldn't stop wondering if anyone had tried using sinusoids instead of B-splines as activation in a KAN, and fortunately/unfortunately that was already the case. I could not find it posted here, so I though I would share in the hope of some insightful discussion. Arxiv: https://arxiv.org/abs/2407.04149 Github repo: https://github.com/ereinha/SineKAN Also what appears to be a peer-reviewed "official" publication here: https://www.mdpi.com/2227-7390/13/19/3157 [link] [comments] |
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#SineKAN
#Kolmogorov-Arnold Networks
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#Sinusoidal Activation Functions
#Activation Functions
#B-splines
#Machine Learning
#Sinusoids
#Neural Networks
#Activation
#Peer-reviewed publication
#Arxiv
#Github
#MDPI
#Function Approximation
#Jacobgorm
#Nonlinear Dynamics
#Deep Learning
#Time Series