We introduce a novel machine learning framework based on recursive autoencoders for sentence-level prediction of sentiment label distributions. Our method learns vector space repr...
Richard Socher, Jeffrey Pennington, Eric H. Huang,...
This paper proposes a data driven image segmentation algorithm, based on decomposing the target output (ground truth). Classical pixel labeling methods utilize machine learning al...
This paper presents a method for visual object categorization based on encoding the joint textural information in objects and the surrounding background, and requiring no segmenta...
Alireza Tavakoli Targhi, Andrzej Pronobis, Heydar ...
We present here a method giving a robust segmentation for in vitro cells observed under standard phase-contrast microscopy. We tackle the problem using the watershed transform. Wa...
In this paper, we present a new co-training strategy that makes use of unlabelled data. It trains two predictors in parallel, with each predictor labelling the unlabelled data for...