content.raw: Really excited to read about Kolmogorov Arnold Networks as a novel alternatives to Multi Layer Perceptrons.
content.type: text
content.value: Really excited to read about Kolmogorov Arnold Networks as a novel alternatives to Multi Layer Perceptrons.
content.type: new_line
content.type: new_line
content.raw: Excerpt:
content.type: text
content.value: Excerpt:
content.type: new_line
content.raw: > Kolmogorov-Arnold Networks (KANs) are promising alternatives of Multi-Layer Perceptrons (MLPs). KANs have strong mathematical foundations just like MLPs: MLPs are based on the universal approximation theorem, while KANs are based on Kolmogorov-Arnold representation theorem. KANs and MLPs are dual: KANs have activation functions on edges, while MLPs have activation functions on nodes. This simple change makes KANs better (sometimes much better!) than MLPs in terms of both model accuracy and interpretability.
content.type: text
content.value: > Kolmogorov-Arnold Networks (KANs) are promising alternatives of Multi-Layer Perceptrons (MLPs). KANs have strong mathematical foundations just like MLPs: MLPs are based on the universal approximation theorem, while KANs are based on Kolmogorov-Arnold representation theorem. KANs and MLPs are dual: KANs have activation functions on edges, while MLPs have activation functions on nodes. This simple change makes KANs better (sometimes much better!) than MLPs in terms of both model accuracy and interpretability.
content.type: new_line
content.type: new_line
content.href: https://github.com/KindXiaoming/pykan
content.raw: https://github.com/KindXiaoming/pykan
content.type: link
rawContent: Really excited to read about Kolmogorov Arnold Networks as a novel alternatives to Multi Layer Perceptrons.
  
  Excerpt:
  > Kolmogorov-Arnold Networks (KANs) are promising alternatives of Multi-Layer Perceptrons (MLPs). KANs have strong mathematical foundations just like MLPs: MLPs are based on the universal approximation theorem, while KANs are based on Kolmogorov-Arnold representation theorem. KANs and MLPs are dual: KANs have activation functions on edges, while MLPs have activation functions on nodes. This simple change makes KANs better (sometimes much better!) than MLPs in terms of both model accuracy and interpretability.
  
  https://github.com/KindXiaoming/pykan
reactions.reaction: 🔥
reactions.users: valeriylo Hardeh AtAndDev
commentators._id: 5f17f0a0925b9863e28ad517
commentators.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/5f17f0a0925b9863e28ad517/fXIY5i9RLsIa1v3CCuVtt.jpeg
commentators.fullname: Victor Mustar
commentators.name: victor
commentators.type: user