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06/19/2022
Geometric Matrix Completion via Sylvester Multi-Graph Neural Network
Despite the success of the Sylvester equation empowered methods on vario...
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05/23/2021
Hypergraph Pre-training with Graph Neural Networks
Despite the prevalence of hypergraphs in a variety of high-impact applic...
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05/12/2021
Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution
Online stores often utilize product relationships such as bundles and su...
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01/16/2014
Most Relevant Explanation in Bayesian Networks
A major inference task in Bayesian networks is explaining why some varia...
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09/26/2013
Evaluating Anytime Algorithms for Learning Optimal Bayesian Networks
Exact algorithms for learning Bayesian networks guarantee to find provab...
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09/26/2013
Solving Limited-Memory Influence Diagrams Using Branch-and-Bound Search
A limited-memory influence diagram (LIMID) generalizes a traditional inf...
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10/19/2012
An Importance Sampling Algorithm Based on Evidence Pre-propagation
Precision achieved by stochastic sampling algorithms for Bayesian networ...
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10/16/2012
An Improved Admissible Heuristic for Learning Optimal Bayesian Networks
Recently two search algorithms, A* and breadth-first branch and bound (B...
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07/11/2012
Annealed MAP
Maximum a Posteriori assignment (MAP) is the problem of finding the most...
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07/04/2012
Importance Sampling in Bayesian Networks: An Influence-Based Approximation Strategy for Importance Functions
One of the main problems of importance sampling in Bayesian networks is ...
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05/09/2012
Most Relevant Explanation: Properties, Algorithms, and Evaluations
Most Relevant Explanation (MRE) is a method for finding multivariate exp...
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03/15/2012
Solving Multistage Influence Diagrams using Branch-and-Bound Search
A branch-and-bound approach to solving influ- ence diagrams has been pre...
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02/14/2012