Novel Deep Gaussian Process Structures with Flexible Depths

Abstract

This paper introduces a novel structure for deep Gaussian processes (DGPs) and a method for determining their depths. The proposed framework enables faster convergence of their parameters and reduces computational cost to optimize them while maintaining performance comparable to that of conventional DGP models. Furthermore, our approach presents a feasible solution to reduce the risk that the model becomes trapped in local minima during the simultaneous training of multiple layers, which ensures more efficient and reliable model training. Through experimental evaluation, we demonstrate the effectiveness of these models and the advantages of integrating transfer learning to reduce retraining costs.