Row 5814

Row ID: 5814 | Dataset Entry | Axioma AXP Content Repository

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When I read the KAN paper, I see physicists casually making fun of the uncertainties in MLPs:

- "The philosophy here is close to the mindset of physicists, who often care more about typical cases rather than worst cases" lol this went hard on NNs

- "Finite grid size can approximate the function well with a residue rate independent of the dimension, hence beating curse of dimensionality!" haha.

- "Neural scaling laws are the phenomenon where test loss decreases with more model parameters"

- "Our approach, which assumes the existence of smooth Kolmogorov Arnold representations, decomposes the high-dimensional function into several 1D functions"

Key Differences With MLPs: - Activation Functions: Unlike MLPs that use fixed activation functions at the nodes, KANs utilize learnable activation functions located on the edges between nodes. - Weight Parameters: In KANs, traditional linear weight matrices are absent. Instead, each weight parameter is replaced by a learnable univariate function, specifically a spline. - Summation Nodes: Nodes in KANs perform simple summation of incoming signals without applying non-linear transformations.

Advantages Over MLPs: - Accuracy: achieve higher accuracy with smaller network sizes compared to larger MLPs in tasks like data fitting and solving partial differential equations (PDEs). - Interpretability: Due to their unique structure, KANs are more interpretable than MLPs.

FieldValue
text When I read the KAN paper, I see physicists casually making fun of the uncertainties in MLPs: - "The philosophy here is close to the mindset of physicists, who often care more about typical cases rather than worst cases" lol this went hard on NNs - "Finite grid size can approximate the function well with a residue rate independent of the dimension, hence beating curse of dimensionality!" haha. - "Neural scaling laws are the phenomenon where test loss decreases with more model parameters" - "…
label r/deeplearning
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communityName r/deeplearning
datetime 2024-05-04
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Raw Record

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  "text": "When I read the KAN paper, I see physicists casually making fun of the uncertainties in MLPs:\n\n- \"The philosophy here is close to the mindset of physicists, who often care more about typical cases rather than worst cases\" lol this went hard on NNs\n\n- \"Finite grid size can approximate the function well with a residue rate independent of the dimension, hence beating curse of dimensionality!\" haha.\n\n- \"Neural scaling laws are the phenomenon where test loss decreases with more model parameters\"\n\n- \"Our approach, which assumes the existence of smooth Kolmogorov Arnold representations, decomposes the high-dimensional function into several 1D functions\"\n\nKey Differences With MLPs:\n- Activation Functions: Unlike MLPs that use fixed activation functions at the nodes, KANs utilize learnable activation functions located on the edges between nodes.\n- Weight Parameters: In KANs, traditional linear weight matrices are absent. Instead, each weight parameter is replaced by a learnable univariate function, specifically a spline. \n- Summation Nodes: Nodes in KANs perform simple summation of incoming signals without applying non-linear transformations.\n\nAdvantages Over MLPs:\n- Accuracy: achieve higher accuracy with smaller network sizes compared to larger MLPs in tasks like data fitting and solving partial differential equations (PDEs).\n- Interpretability: Due to their unique structure, KANs are more interpretable than MLPs.",
  "label": "r/deeplearning",
  "dataType": "post",
  "communityName": "r/deeplearning",
  "datetime": "2024-05-04",
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