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About Redatuming Physical Systems Using Symmetric Autoencoders: Formerly at Massachusetts Institute of Technology, USA
This paper considers physical systems where repeated measurements of hidden states are corrupted by unmodeled nuisance parameters. It proposes using a neural network autoencoder called SymAE to disentangle coherent information about the underlying state from incoherent nuisance information. This allows creating "virtual data instances" through "redatuming", which combines the coherent information from one measurement with nuisance information from another to engineer instances that share nuisance information. This may help analyze differences in physical states despite uncertainties in the measurement process.
Detailed Information
| Author: | ['Washim Akram'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 13 |
| Language: | English |
| Format: | |
| Price: | FREE |
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