In this weekly meeting the CVG members come together and discuss recent topics in the Computer Vision and Machine Learning community. In addition there are typically two presentations of selected papers or student projects. 

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.

In this weekly meeting the CVG members come together and discuss recent topics in the Computer Vision and Machine Learning community. In addition there are typically two presentations of selected papers or student projects. 

In this weekly meeting the CVG members come together and discuss recent topics in the Computer Vision and Machine Learning community. In addition there are typically two presentations of selected papers or student projects. 

In this weekly meeting the CVG members come together and discuss recent topics in the Computer Vision and Machine Learning community. In addition there are typically two presentations of selected papers or student projects.