Are dropout networks a type of dynamic computation graph with scaled weights during inference? I just want to confirm this intuition. What are the subtle differences? Please also comment if you know of any literature on DCGs other than the following papers:

Deep Learning with Dynamic Computation Graphs,
DyNet: The Dynamic Neural Network Toolkit
On-the-fly Operation Batching in Dynamic Computation Graphs
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks
Learning task-dependent distributed representations by backpropagation through structure
DSD: Dense-Sparse-Dense Training for Deep Neural Networks
Dynamic Graph Convolutional Networks
Deciding How to Decide: Dynamic Routing in Artificial Neural Networks
Dropout: A Simple Way to Prevent Neural Networks from Overfitting
FreezeOut: Accelerate Training by Progressively Freezing Layers
Swapout: Learning an ensemble of deep architectures
Deep Networks with Stochastic Depth
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations
Regularization of neural networks using dropconnect
Evolving Neural Networks through Augmenting Topologies
Any other Kenneath Stanley papers (I’ve collected most of them)

Thanks