TECHNOLOGY FOR MEMORY-EFFICIENT AND PARAMETER-EFFICIENT GRAPH NEURAL NETWORKS

摘要

Systems, apparatuses and methods may provide for technology that trains a reversible graph neural network (GNN) by partitioning an input vertex feature matrix into a plurality of groups, generating, via a block of the reversible GNN, outputs for the plurality of groups based on an adjacency matrix and an edge feature matrix, wherein the outputs are generated during one or more forward propagations, conducting a reconstruction of the input feature matrix during one or more backward propagations, and excluding the adjacency matrix and the edge feature matrix from the reconstruction. The technology also trains a deep equilibrium GNN.