253 citations · 578 across the 15 of their papers we have counts for
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Adversarial Alignment of Class Prediction Uncertainties for Domain Adaptation
Jeroen Manders, Twan van Laarhoven, Elena Marchiori
We consider unsupervised domain adaptation: given labelled examples from a source domain and unlabelled examples from a related target domain, the goal is to infer the labels of ta…
Domain Adaptation with Randomized Expectation Maximization
Twan van Laarhoven, Elena Marchiori
Domain adaptation (DA) is the task of classifying an unlabeled dataset (target) using a labeled dataset (source) from a related domain. The majority of successful DA methods try to…
Unsupervised Domain Adaptation with Random Walks on Target Labelings
Twan van Laarhoven, Elena Marchiori
Unsupervised Domain Adaptation (DA) is used to automatize the task of labeling data: an unlabeled dataset (target) is annotated using a labeled dataset (source) from a related doma…
Resolution-limit-free and local Non-negative Matrix Factorization quality functions for graph clustering
Twan van Laarhoven, Elena Marchiori
Many graph clustering quality functions suffer from a resolution limit, the inability to find small clusters in large graphs. So called resolution-limit-free quality functions do n…