no code implementations • NeurIPS 2015 • David B. Smith, Vibhav G. Gogate
Given a schematic-based representation of an SRM, we show how to efficiently compute a tight upper bound on the time and space cost of exact inference from a current assignment and the remaining schematic.
no code implementations • NeurIPS 2015 • Happy Mittal, Anuj Mahajan, Vibhav G. Gogate, Parag Singla
Lifted inference rules exploit symmetries for fast reasoning in statistical rela-tional models.
no code implementations • NeurIPS 2015 • Somdeb Sarkhel, Parag Singla, Vibhav G. Gogate
A key advantage of these lifted algorithms is that they have much smaller computational complexity than propositional algorithms when symmetries are present in the MLN and these symmetries can be detected using lifted inference rules.
no code implementations • NeurIPS 2014 • Deepak Venugopal, Vibhav G. Gogate
Second, they suffer from the evidence problem, which arises because evidence breaks symmetries, severely diminishing the power of lifted inference.
no code implementations • NeurIPS 2014 • Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav G. Gogate
In this paper, we present a new approach for lifted MAP inference in Markov logic networks (MLNs).
no code implementations • NeurIPS 2014 • Happy Mittal, Prasoon Goyal, Vibhav G. Gogate, Parag Singla
In this paper, we present two new lifting rules, which enable fast MAP inference in a large class of MLNs.