Difference between revisions of "Discussion of: An Automatic Framework to Build Neural Network-based Surrogate for High-Performance Computing Applications"

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(Created page with " David Notes: * Use of "Customized Autoencoder" to sparsify inputs is interesting. ** Are `const` parameters the primary values being filtered? * User inputs place boundaries...")
 
 
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David Notes:
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== David Notes ==
 
* Use of "Customized Autoencoder" to sparsify inputs is interesting.  
 
* Use of "Customized Autoencoder" to sparsify inputs is interesting.  
 
** Are `const` parameters the primary values being filtered?
 
** Are `const` parameters the primary values being filtered?
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** How is Bayesian used? (what are priors?)
 
** How is Bayesian used? (what are priors?)
 
* Feature to introduce perturbation into input features in cases where insufficient variation of data are available.(section 3.1, Step 3)
 
* Feature to introduce perturbation into input features in cases where insufficient variation of data are available.(section 3.1, Step 3)
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* μ = 10% for hit rate test.
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== Cissie ==
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* Slides: [https://docs.google.com/presentation/d/1Z5SXj7-yfROGzdTcfkIgT6Tg1s1a5sANaa1rxGvTHTA Auto-HPCNet vs PHASM]
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* Related Github folder by the 1st author: https://github.com/wdong5/AutoHPC-autoencoder

Latest revision as of 17:15, 9 August 2023

David Notes

  • Use of "Customized Autoencoder" to sparsify inputs is interesting.
    • Are `const` parameters the primary values being filtered?
  • User inputs place boundaries on accuracy and speed when optimizing topology and input features.
    • How is Bayesian used? (what are priors?)
  • Feature to introduce perturbation into input features in cases where insufficient variation of data are available.(section 3.1, Step 3)
  • μ = 10% for hit rate test.

Cissie