tilings in time roughly linear to the number of candidate tile locations significantly outperforming traditional combinatorial search our learned to tile approach given an input tileset we first enumerate candidate tile locations then we generate random shapes to crop and locate candidate tile locations after that we create a graph to describe each tile placement and train our network to predict tile placements with our self supervised loss at test time we applied the Train Network to predict tile locations for