The DeepNets-1M dataset is composed of neural network architectures represented as graphs where nodes are operations (convolution, pooling, etc.) and edges correspond to the forward pass flow of data through The DeepNets-1M is used to train and evaluate parameter prediction models such as Graph HyperNetworks. These models can predict all parameters for a given network (graph) in a single forward pass and the results can be compared to optimizing parameters with SGD.
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