4 citations · 11 across the 14 of their papers we have counts for
5 papers · 1 filter
Learning an MR acquisition-invariant representation using Siamese neural networks
Wouter M. Kouw, Marco Loog, Wilbert Bartels +1
Generalization of voxelwise classifiers is hampered by differences between MRI-scanners, e.g. different acquisition protocols and field strengths. To address this limitation, we pr…
Distance Based Source Domain Selection for Sentiment Classification
Lex Razoux Schultz, Marco Loog, Peyman Mohajerin Esfahani
Automated sentiment classification (SC) on short text fragments has received increasing attention in recent years. Performing SC on unseen domains with few or no labeled samples ca…
Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features
Julius von Kügelgen, Alexander Mey, Marco Loog
Current methods for covariate-shift adaptation use unlabelled data to compute importance weights or domain-invariant features, while the final model is trained on labelled data onl…
Single Shot Active Learning using Pseudo Annotators
Yazhou Yang, Marco Loog
Standard myopic active learning assumes that human annotations are always obtainable whenever new samples are selected. This, however, is unrealistic in many real-world application…
Effects of sampling skewness of the importance-weighted risk estimator on model selection
Wouter M. Kouw, Marco Loog
Importance-weighting is a popular and well-researched technique for dealing with sample selection bias and covariate shift. It has desirable characteristics such as unbiasedness, c…