  Deep learning has become the state-of-art tool in many applications, but the
evaluation and training of deep models can be time-consuming and
computationally expensive. The conditional computation approach has been
proposed to tackle this problem (Bengio et al., 2013; Davis & Arel, 2013). It
operates by selectively activating only parts of the network at a time. In this
paper, we use reinforcement learning as a tool to optimize conditional
computation policies. More specifically, we cast the problem of learning
activation-dependent policies for dropping out blocks of units as a
reinforcement learning problem. We propose a learning scheme motivated by
computation speed, capturing the idea of wanting to have parsimonious
activations while maintaining prediction accuracy. We apply a policy gradient
algorithm for learning policies that optimize this loss function and propose a
regularization mechanism that encourages diversification of the dropout policy.
We present encouraging empirical results showing that this approach improves
the speed of computation without impacting the quality of the approximation.
