11 citations · 11 across the 5 of their papers we have counts for
5 papers
Learning to Guide Local Search for MPE Inference in Probabilistic Graphical Models
Brij Malhotra, Shivvrat Arya, Tahrima Rahman +1
Most Probable Explanation (MPE) inference in Probabilistic Graphical Models (PGMs) is a fundamental yet computationally challenging problem arising in domains such as diagnosis, pl…
Learning to Condition: A Neural Heuristic for Scalable MPE Inference
Brij Malhotra, Shivvrat Arya, Tahrima Rahman +1
We introduce learning to condition (L2C), a scalable, data-driven framework for accelerating Most Probable Explanation (MPE) inference in Probabilistic Graphical Models (PGMs), a f…
Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models
Shivvrat Arya, Tahrima Rahman, Vibhav Gogate
We propose a self-supervised learning approach for solving the following constrained optimization task in log-linear models or Markov networks. Let and be two log-linear mo…
Neural Network Approximators for Marginal MAP in Probabilistic Circuits
Shivvrat Arya, Tahrima Rahman, Vibhav Gogate
Probabilistic circuits (PCs) such as sum-product networks efficiently represent large multi-variate probability distributions. They are preferred in practice over other probabilist…
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition
Mahsan Nourani, Chiradeep Roy, Tahrima Rahman +3
Explainable machine learning and artificial intelligence models have been used to justify a model's decision-making process. This added transparency aims to help improve user perfo…