Deep Probabilistic Generative Models

With the development of neural networks and increased computational power, deep generative modeling has emerged as one of the leading directions in AI. We are shifting from traditional discriminative tasks (such as classification, segmentation, or clustering), which focus on modeling conditional distributions, to a more comprehensive framework aimed at modeling the joint distribution of the data itself. Discriminative models alone can be insufficient for robust decision-making and the development of intelligent systems, as it is also necessary to understand the underlying data-generating process and be able to express uncertainty about the environment.

Typically, in deep learning literature, generative models are viewed as methods for synthesizing new data. However, in this seminar, we will adopt a probabilistic perspective to highlight that modeling the marginal likelihood of the data has much broader applicability, and this could be essential for building successful AI systems.

In this seminar, we will ask ourselves how to formulate deep generative models (i.e., how to express and learn the marginal likelihood of the data) and explore the different approaches proposed in the literature. The aim is for students to critically assess existing methods, understand their strengths and limitations, and identify potential directions for future research.

More info

https://cms.sic.saarland/deeppgms/