What is an example of using Hessian Vector Product in Learning using Pytorch?

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I was looking at the famous paper MAML (Model Agnostic Meta-Learning) and they say they use Hessian-Vector products. How do they use them:

  1. in mathematics? i.e. how do the Hessian vector products get involved in the update rule?

  2. in the code. How do they update the parameters being learned?

Or is there a nice example of how Hessian Vector products are used in (meta) training?

cross posted:

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