9 citations · 25 across the 8 of their papers we have counts for
4 papers · 1 filter
Training a First-Order Theorem Prover from Synthetic Data
Vlad Firoiu, Eser Aygun, Ankit Anand +6
A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in training successful deep learning models.…
Block-Value Symmetries in Probabilistic Graphical Models
Gagan Madan, Ankit Anand, Mausam +1
One popular way for lifted inference in probabilistic graphical models is to first merge symmetric states into a single cluster (orbit) and then use these for downstream inference,…
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…
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…