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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* 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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| + | == Cissie == | ||
| + | * Slides: [https://docs.google.com/presentation/d/1Z5SXj7-yfROGzdTcfkIgT6Tg1s1a5sANaa1rxGvTHTA Auto-HPCNet vs PHASM] | ||
Revision as of 16:35, 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)
Cissie
- Slides: Auto-HPCNet vs PHASM