Uber AI Labs has developed an algorithm called Generative Teaching Networks (GTN) that produces synthetic training data for neural networks which allows the networks to be trained faster than when using real data. Using this synthetic data, Uber sped up its neural architecture search (NAS) deep-learning optimization process by 9x. In a paper published on arXiv, the team described the system and a series of experiments. GTN is motivated by the problem of neural architecture search (NAS), which trains many different deep-learning model structures and selects the one that performs best on a set of test data. While a typical approach would train each model on the full data set for multiple iterations (or epochs), this is time-consuming and expensive.