This work proposes a novel Graph-based neural ArchiTecture Encoding Scheme, aka
GATES, to improve the predictor-based neural architecture search. Specifically, different
from existing graph-based schemes, GATES models the operations as the transformation of
the propagating information, which mimics the actual data processing of neural architecture.
GATES is a more reasonable modeling of the neural architectures, and can encode
architectures from both the “operation on node” and “operation on edge” cell search spaces …

In practice, to calculate the information propagation following the topological order of
different graphs in a batched manner, we use a stack of GATES layers. In the forward
process of each GATES layer, one step of information propagation is taken place at every
node. The detailed formulas and implementations of one GATES layer for “operation on
node” and “operation on edge” search spaces are shown as follows, and the notations are
summarized in Table. 1. Operation On Node (OON) Search Space For the OON case, we …