Workshop on "Probabilistic Deep Generative Models" by Prof. Sriraam Natarajan_ Day 2

Day 2: Probabilistic Circuits as Expressive and Tractable Generative Models
-Popular generative models & their tractability for probabilistic inference,
-Probabilistic Circuits: Representation and Inference
Representing probability distributions as computational graphs
Achieving tractability for inference queries via structural properties
-Learning Probabilistic Circuits
Structure and parameter learning
Random Tensorized Structures, Knowledge Intensive Learning

Abstract:
Probabilistic models deal with uncertainty in data-driven decision-making and modeling in a principled manner. However, as models and datasets grow in complexity, efficient and exact inference (querying the model) becomes a challenge, hindering their feasibility in high-stakes domains like healthcare. This tutorial aims to introduce participants to Deep and Tractable Probabilistic Generative Models, a special class of generative models that balance expressiveness and tractability. Participants will learn about their theoretical foundations, practical implementations, and real-world applications.

More info about the event at https://rbcdsai.iitm.ac.in/events/rbc...

For more information about the exciting work and opportunities please visit:
https://wsai.iitm.ac.in/
https://rbcdsai.iitm.ac.in/