Probabilistic models and Monte Carlo objectives in generative modelling - Shi, Y (2023)
Generative modelling, i.e. learning probability distributions of interest, is a key and challenging problem in statistics and machine learning. With recent breakthroughs in deep learning, there have been remarkable advances in applying novel machine learning methods to solve generative modelling problems. In particular, with developments in variational inference, the latent variable model (LVM) has become a powerful class of generative models capable of modelling complex distributions flexibly. Recently, the denoising diffusion model (DDM) has emerged as another state-of-the-art generative model, which can also be viewed under the LVM framework but provides even higher expressivity and quality of learning and achieved exceptional results in many tasks such as unsupervised learning of images.