Project: Scalable Implementation of Probabilistic Circuits
Description
This internal project aims at designing and development a usable software package for learning and reasoning with probabilistic circuits. Probabilistic circuits are models which can represent complicated mixture models and their computation circuit can be wide and deep. Because they have a structure which is learned from data, scalable parallel implementations are not trivial but essential for the dissemination of the models.
This project requires good knowledge of programming languages and coding, including C/C++, python (optional), R (optional), and interest in writing efficient and good quality code.
References:
http://starai.cs.ucla.edu/papers/LecNoAAAI20.pdf
http://starai.cs.ucla.edu/papers/ProbCirc20.pdf
https://www.youtube.com/watch?v=2RAG5-L9R70
Details
- Supervisor
-
Cassio de Campos
- Interested?
- Get in contact