Speaker
Péter Antal
(BME)
Description
Deep probabilistic generative models demonstrated superior and scalable performance in multiple domains. However, their application in biomedicine is still hindered by the following challenges, incorporation of prior knowledge, interpretation and explanation, and learning from highly incomplete data, especially from sparsely populated time-series data. At first, I illustrate standard solutions to these challenges using belief networks. Next, I overview the evolution of generative models from deep belief networks. Finally, I summarize recent extensions of deep generative models to cope with these challenges and their biomedical applications.
Primary author
Péter Antal
(BME)
