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Deep learning has become the state-of-art tool in many applications, but theevaluation and training of deep models can be time-consuming andcomputationally expensive. The conditional computation approach has beenproposed to tackle this problem (Bengio et al., 2013; Davis & Arel, 2013). Itoperates by selectively activating only parts of the network at a time. In thispaper, we use reinforcement learning as a tool to optimize conditionalcomputation policies. More specifically, we cast the problem of learningactivation-dependent policies for dropping out blocks of units as areinforcement learning problem. We propose a learning scheme motivated bycomputation speed, capturing the idea of wanting to have parsimoniousactivations while maintaining prediction accuracy. We apply a policy gradientalgorithm for learning policies that optimize this loss function and propose aregularization mechanism that encourages diversification of the dropout policy.We present encouraging empirical results showing that this approach improvesthe speed of computation without impacting the quality of the approximation.

Conditional Computation in Neural Networks for faster models