# Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs by Selective Execution

@inproceedings{Liu2017DynamicDN, title={Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs by Selective Execution}, author={Lanlan Liu and Jia Deng}, booktitle={AAAI Conference on Artificial Intelligence}, year={2017} }

We introduce Dynamic Deep Neural Networks (D2NN), a new type of feed-forward deep neural network that allows selective execution. [] Key Method To achieve dynamic selective execution, a D2NN augments a feed-forward deep neural network (directed acyclic graph of differentiable modules) with controller modules. Each controller module is a sub-network whose output is a decision that controls whether other modules can execute. A D2NN is trained end to end.

## 148 Citations

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This study introduces the simple yet effective concept of decision gates (d-gate), modules trained to decide whether a sample needs to be projected into a deeper embedding or if an early prediction can be made at the d-gate, thus enabling the computation of dynamic representations at different depths.

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- 2018

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### Efficient Inference on Deep Neural Networks by Dynamic Representations and Decision Gates

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This study introduces the concept of decision gates (d-gate), modules trained to decide whether a sample needs to be projected into a deeper embedding or if an early prediction can be made at the d-gate, thus enabling the computation of dynamic representations at different depths.

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