Search Results for author:
Found 3 papers, 1 papers with code
Multi-source Attention for Unsupervised Domain Adaptation
We model source-selection as an attention-learning problem, where we learn attention over sources for a given target instance.
General Classification
Multi-Source Unsupervised Domain Adaptation
+3
Self-Adaptation for Unsupervised Domain Adaptation
Using the labelled data from the source domain, we first learn a projection that maximises the distance among the nearest neighbours with opposite labels in the source domain.
Solving Feature Sparseness in Text Classification using Core-Periphery Decomposition
We observe that prioritising features that are common to both training and test instances as cores during the CP decomposition to further improve the accuracy of text classification.
