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Found 9 papers, 4 papers with code
The Utility of "Even if..." Semifactual Explanation to Optimise Positive Outcomes
When users receive either a positive or negative outcome from an automated system, Explainable AI (XAI) has almost exclusively focused on how to mutate negative outcomes into positive ones by crossing a decision boundary using counterfactuals (e. g., \textit{"If you earn 2k more, we will accept your loan application"}).
Rényi Divergence Deep Mutual Learning
This paper revisits Deep Mutual Learning (DML), a simple yet effective computing paradigm.
Posterior Regularization on Bayesian Hierarchical Mixture Clustering
Bayesian hierarchical mixture clustering (BHMC) improves traditionalBayesian hierarchical clustering by replacing conventional Gaussian-to-Gaussian kernels with a Hierarchical Dirichlet Process Mixture Model(HDPMM) for parent-to-child diffusion in the generative process.
Towards Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning
The ambiguous annotation criteria lead to divergence of Chinese Word Segmentation (CWS) datasets in various granularities.
Partially Observable Markov Decision Process Modelling for Assessing Hierarchies
Hierarchical clustering has been shown to be valuable in many scenarios.
BERT-Based Multi-Head Selection for Joint Entity-Relation Extraction
Combining these three contributions, we enhance the information extracting ability of the multi-head selection model and achieve F1-score 0. 876 on testset-1 with a single model.
$t$-$k$-means: A Robust and Stable $k$-means Variant
$k$-means algorithm is one of the most classical clustering methods, which has been widely and successfully used in signal processing.
Inferring Hierarchical Mixture Structures: A Bayesian Nonparametric Approach
This paper focuses on the problem of hierarchical non-overlapping clustering of a dataset.
Toward Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning
The ambiguous annotation criteria lead to divergence of Chinese Word Segmentation (CWS) datasets in various granularities.
