Deep probabilistic modelling with Pyro
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, Chi Nhan Nguyen demonstrates 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. He gives an introduction to probabilistic and deep probabilistic modelling using the scalable probabilistic programming language Pyro, which runs on top of PyTorch.
He also demonstrates some real-world examples where the results clearly benefit from a probabilistic approach.
Chi Nhan Nguyen works as Senior Data Scientist Consultant at itemis. As a high-energy physicist, he has spent many years researching at facilities such as CERN, Fermilab and DESY and has implemented and used machine learning techniques for data analysis. For several years, he has been working as a data scientist in the private sector, where he focuses on developing probabilistic models and deep learning methods.