University of Cambridge > > Machine Learning @ CUED > Generative probabilistic programming: applications and new ideas

Generative probabilistic programming: applications and new ideas

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Zoubin Ghahramani.

Probabilistic programming has recently attracted much attention in Computer Science and Machine Learning communities. I will briefly demonstrate two generative probabilistic graphics programs (models), which I contributed to develop. Then I will present ideas on two research directions I am interested in pursuing: a path to scaling up general-purpose approximate inference in probabilistic programs using parallelism, and a path to automatic programming via general-purpose approximate inference.

This is based on joint work with Frank Wood, Vikash Mansinghka, Tejas Kulkarni, Daniel Selsam, Joshua Tenenbaum, et al.

This talk is part of the Machine Learning @ CUED series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


© 2006-2024, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity