META-LEARNING NEURAL ARCHITECTURE SEARCH VIA GRAPH NETWORKS ON SEARCH SPACE LATTICES

摘要

One or more embodiments of the disclosure include systems and methods that use meta-learning to learn how to optimally find a new neural network architecture for a task using past architectures that were optimized for other tasks, including for example tasks associated with autonomous, semi-autonomous, assisted, or other driving applications. A computer implemented method of the disclosure includes configuring a search space lattice comprising nodes representing operator choices, edges, and a maximum depth. The method includes defining an objective function. The method further includes configuring a graph network over the search space lattice to predict edge weights over the search space lattice. The method also includes alternating optimization between (1) weights of the graph network, to optimize the objective function over a validation set, and (2) weights corresponding to nodes of the search space lattice that are randomly initialized or configured using previously trained paths in the search space lattice.