Conditional Computation in Neural Networks for faster models

Understand

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.