Imitation Training for Machine Learning Systems with Synthetic Data Generators

Various systems and methods are described herein for improving the aggressive development of machine learning systems. In machine learning, there is always a trade-off between allowing a machine learning system to learn as much as it can from training data and overfitting on the training data. This trade-off is important because overfitting usually causes performance on new data to be worse. However, various systems and methods can be utilized to separate the process of detailed learning and knowledge acquisition and the process of imposing restrictions and smoothing estimates, thereby allowing machine learning systems to aggressively learn from training data, while mitigating the effects of overfitting on the training data.