Using synthetic data to train neural networks is model-based reasoning
We draw a formal connection between using synthetic training data to optimize neural network parameters and approximate, Bayesian, model-based reasoning. In particular, training a neural network using synthetic data can be viewed as learning a proposal distribution generator for approximate inferenc...
| Main Authors: | , , , |
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| Format: | Conference item |
| Published: |
IEEE
2017
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