5 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.AI2017
Non-Count Symmetries in Boolean & Multi-Valued Prob. Graphical Models
Ankit Anand, Ritesh Noothigattu, Parag Singla +1
Lifted inference algorithms commonly exploit symmetries in a probabilistic graphical model (PGM) for efficient inference. However, existing algorithms for Boolean-valued domains ca…
cs.CV2017
Coarse-to-Fine Lifted MAP Inference in Computer Vision
Haroun Habeeb, Ankit Anand, Mausam +1
There is a vast body of theoretical research on lifted inference in probabilistic graphical models (PGMs). However, few demonstrations exist where lifting is applied in conjunction…
cs.AI2016★ 5 cited
Contextual Symmetries in Probabilistic Graphical Models
Ankit Anand, Aditya Grover, Mausam +1
An important approach for efficient inference in probabilistic graphical models exploits symmetries among objects in the domain. Symmetric variables (states) are collapsed into met…