  Methods for neural network hyperparameter optimization and meta-modeling are
computationally expensive due to the need to train a large number of model
configurations. In this paper, we show that standard frequentist regression
models can predict the final performance of partially trained model
configurations using features based on network architectures, hyperparameters,
and time-series validation performance data. We empirically show that our
performance prediction models are much more effective than prominent Bayesian
counterparts, are simpler to implement, and are faster to train. Our models can
predict final performance in both visual classification and language modeling
domains, are effective for predicting performance of drastically varying model
architectures, and can even generalize between model classes. Using these
prediction models, we also propose an early stopping method for hyperparameter
optimization and meta-modeling, which obtains a speedup of a factor up to 6x in
both hyperparameter optimization and meta-modeling. Finally, we empirically
show that our early stopping method can be seamlessly incorporated into both
reinforcement learning-based architecture selection algorithms and bandit based
search methods. Through extensive experimentation, we empirically show our
performance prediction models and early stopping algorithm are state-of-the-art
in terms of prediction accuracy and speedup achieved while still identifying
the optimal model configurations.
