More talks in the program:
16:30 - 17:30
The success of deep neural networks in diverse areas as image recognition and natural language processing has been outstanding in recent years. However, classical machine learning and deep learning algorithms can only propose the most probable solutions and are not able to adequately model uncertainty.
In this talk, I will demonstrate how appropriate modelling of uncertain knowledge and reasoning leads to more informative results that can be used for better decision making. Recently, there has been a lot of progress in combining the probabilistic paradigm with deep neural architectures. In the past, computational probabilistic methods and tools lack the scalability and flexibility when it comes to large data sets and high-dimensional models. I will give an introduction to probabilistic and deep probabilistic modelling using the scalable probabilistic programming language Pyro which runs on top of PyTorch. I will also show you real-world examples where the results clearly benefit from a probabilistic approach.