Generative model, as an unsupervised learning approach, is a promising development for learning meaningful representations without focusing on specific tasks. Finding such generative models is one of the most fundamental problems in both statistics, computer vision, and artificial intelligence research. The deep energy-based model (EBM) is one of the most promising candidates. Previous works have proven the capability of EBM on image domains. In this dissertation, we explore the capability of EBM in three important domains: unordered set modeling, 3D shape representation, and continuous inverse optimal control. For each domain, we proposed a novel approach using EBM and got substantial competitive results.