Learning generative probabilistic models that can estimate the continuous density given a set of samples, and that can sample from that density, is one of the fundamental challenges in unsupervised machine learning. In this talk I will describe our new approach to obtain such models based on denoising density estimators (DDEs). A DDE is a scalar function, parameterized by a neural network, that is efficiently trained to represent a kernel density estimator of the data. Leveraging DDEs, I will show how we can develop a novel approach to obtain generative models that sample from given densities. Finally I will discuss other applications and opportunities based on our proposed denoising density estimator.