25 citations · 27 across the 9 of their papers we have counts for
9 papers
MCAC: A Model Counting Algorithm for Exact Computation of Error Metrics of Approximate Circuits
S Ramprasath, Sibi Siddharthan, Marrivada Gopala Krishna Sai Charan +1
Effective usage of approximate circuits for various performance trade-offs requires accurate computation of error. MCAC is a novel model counting framework for exact computation of…
Approximate inference of marginals using the IBIA framework
Shivani Bathla, Vinita Vasudevan
Exact inference of marginals in probabilistic graphical models (PGM) is known to be intractable, necessitating the use of approximate methods. Most of the existing variational tech…
IBIA: An Incremental Build-Infer-Approximate Framework for Approximate Inference of Partition Function
Shivani Bathla, Vinita Vasudevan
Exact computation of the partition function is known to be intractable, necessitating approximate inference techniques. Existing methods for approximate inference are slow to conve…
MPE inference using an Incremental Build-Infer-Approximate Paradigm
Shivani Bathla, Vinita Vasudevan
Exact inference of the most probable explanation (MPE) in Bayesian networks is known to be NP-complete. In this paper, we propose an algorithm for approximate MPE inference that is…
IBIA: Bayesian Inference via Incremental Build-Infer-Approximate operations on Clique Trees
Shivani Bathla, Vinita Vasudevan
Exact inference in Bayesian networks is intractable and has an exponential dependence on the size of the largest clique in the corresponding clique tree (CT), necessitating approxi…
Fast and Accurate Proper Orthogonal Decomposition using Efficient Sampling and Iterative Techniques for Singular Value Decomposition
Charumathi V, M. Ramakrishna, Vinita Vasudevan
In this paper, we propose a computationally efficient iterative algorithm for proper orthogonal decomposition (POD) using random sampling based techniques. In this algorithm, addit…