[R] Greg Yang's work on a rigorous mathematical theory for neural networks

• GP4A

  3 231 10.0 Jupyter Notebook

  Code for NeurIPS 2019 paper: "Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes"

took a quick glance (https://arxiv.org/abs/1910.12478 and https://proceedings.mlr.press/v139/yang21c.html), a few theorems but where r the proofs?

• mup

  12 1,169 3.8 Jupyter Notebook

  maximal update parametrization (µP)

  

Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes: https://arxiv.org/abs/1910.12478 Tensor Programs II: Neural Tangent Kernel for Any Architecture: https://arxiv.org/abs/2006.14548 Tensor Programs III: Neural Matrix Laws: https://arxiv.org/abs/2009.10685 Tensor Programs IV: Feature Learning in Infinite-Width Neural Networks: https://proceedings.mlr.press/v139/yang21c.html Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer: https://arxiv.org/abs/2203.03466

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• NTK4A

  2 90 10.0 Jupyter Notebook

  Code for the paper: "Tensor Programs II: Neural Tangent Kernel for Any Architecture"

Found relevant code at https://github.com/thegregyang/NTK4A + all code implementations here

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