no code implementations • • John Winn, Matteo Venanzi, Tom Minka, Ivan Korostelev, John Guiver, Elena Pochernina, Pavel Mishkov, Alex Spengler, Denise Wilkins, Sian Lindley, Richard Banks, Sam Webster, Yordan Zaykov
The knowledge discovery process uses a probabilistic program defining the process of generating the data item from a set of unknown typed entities.
We develop a Learning Direct Optimization (LiDO) method for the refinement of a latent variable model that describes input image x.
The use of a probabilistic program allows uncertainty in the text to be propagated through to the retrieved facts, which increases accuracy and helps merge facts from multiple sources.
Randomized decision trees and forests have a rich history in machine learning and have seen considerable success in application, perhaps particularly so for computer vision.