25–26 Nov 2019
Hotel Mercure Budapest
Europe/Budapest timezone

Learning structured deep generative models from incomplete data in biomedicine

25 Nov 2019, 16:25
25m
Mátyás Hall (Groundfloor) (Hotel Mercure Budapest)

Mátyás Hall (Groundfloor)

Hotel Mercure Budapest

Krisztina körút 41-43. 1013 Budapest Hungary
Lecture

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

Presentation materials