Discussion of: An Automatic Framework to Build Neural Network-based Surrogate for High-Performance Computing Applications
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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
- Slides: Auto-HPCNet vs PHASM
- Related Github folder by the 1st author: https://github.com/wdong5/AutoHPC-autoencoder