  An important step in the task of neural network design, such as
hyper-parameter optimization (HPO) or neural architecture search (NAS), is the
evaluation of a candidate model's performance. Given fixed computational
resources, one can either invest more time training each model to obtain more
accurate estimates of final performance, or spend more time exploring a greater
variety of models in the configuration space. In this work, we aim to optimize
this exploration-exploitation trade-off in the context of HPO and NAS for image
classification by accurately approximating a model's maximal performance early
in the training process. In contrast to recent accelerated NAS methods
customized for certain search spaces, e.g., requiring the search space to be
differentiable, our method is flexible and imposes almost no constraints on the
search space. Our method uses the evolution history of features of a network
during the early stages of training to build a proxy classifier that matches
the peak performance of the network under consideration. We show that our
method can be combined with multiple search algorithms to find better solutions
to a wide range of tasks in HPO and NAS. Using a sampling-based search
algorithm and parallel computing, our method can find an architecture which is
better than DARTS and with an 80% reduction in wall-clock search time.
