  Machine learning models are often used at test-time subject to constraints
and trade-offs not present at training-time. For example, a computer vision
model operating on an embedded device may need to perform real-time inference,
or a translation model operating on a cell phone may wish to bound its average
compute time in order to be power-efficient. In this work we describe a
mixture-of-experts model and show how to change its test-time resource-usage on
a per-input basis using reinforcement learning. We test our method on a small
MNIST-based example.
