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Found 8 papers, 1 papers with code
Context-Aware Clustering using Large Language Models
Thus, we propose CACTUS (Context-Aware ClusTering with aUgmented triplet losS), a systematic approach that leverages open-source LLMs for efficient and effective supervised clustering of entity subsets, particularly focusing on text-based entities.
Geometric Matrix Completion via Sylvester Multi-Graph Neural Network
Despite the success of the Sylvester equation empowered methods on various graph mining applications, such as semi-supervised label learning and network alignment, there also exists several limitations.
Hypergraph Pre-training with Graph Neural Networks
Despite the prevalence of hypergraphs in a variety of high-impact applications, there are relatively few works on hypergraph representation learning, most of which primarily focus on hyperlink prediction, often restricted to the transductive learning setting.
Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution
In this case, the problem becomes a classification task on weighted graphs and represents an interesting application area for modern tools such as Graph Neural Networks (GNNs).
Most Relevant Explanation in Bayesian Networks
In this paper, we introduce a method called Most Relevant Explanation (MRE) which finds a partial instantiation of the target variables that maximizes the generalized Bayes factor (GBF) as the best explanation for the given evidence.
Solving Limited-Memory Influence Diagrams Using Branch-and-Bound Search
A limited-memory influence diagram (LIMID) generalizes a traditional influence diagram by relaxing the assumptions of regularity and no-forgetting, allowing a wider range of decision problems to be modeled.
Evaluating Anytime Algorithms for Learning Optimal Bayesian Networks
Exact algorithms for learning Bayesian networks guarantee to find provably optimal networks.
Empirical evaluation of scoring functions for Bayesian network model selection
In this work, we empirically evaluate the capability of various scoring functions of Bayesian networks for recovering true underlying structures.
