30 citations · 73 across the 6 of their papers we have counts for
5 papers · 1 filter
Characterizing multiple instance datasets
Veronika Cheplygina, David M. J. Tax
In many pattern recognition problems, a single feature vector is not sufficient to describe an object. In multiple instance learning (MIL), objects are represented by sets (\emph{b…
Unsupervised Learning of Sequence Representations by Autoencoders
Wenjie Pei, David M. J. Tax
Sequence data is challenging for machine learning approaches, because the lengths of the sequences may vary between samples. In this paper, we present an unsupervised learning mode…
Label Stability in Multiple Instance Learning
Veronika Cheplygina, Lauge Sørensen, David M. J. Tax +2
We address the problem of \emph{instance label stability} in multiple instance learning (MIL) classifiers. These classifiers are trained only on globally annotated images (bags), b…
Classification of COPD with Multiple Instance Learning
Veronika Cheplygina, Lauge Sørensen, David M. J. Tax +3
Chronic obstructive pulmonary disease (COPD) is a lung disease where early detection benefits the survival rate. COPD can be quantified by classifying patches of computed tomograph…
Modeling Time Series Similarity with Siamese Recurrent Networks
Wenjie Pei, David M. J. Tax, Laurens van der Maaten
Traditional techniques for measuring similarities between time series are based on handcrafted similarity measures, whereas more recent learning-based approaches cannot exploit ext…