Clinical data management systems typically provide caregiver teams with useful information,
derived from large, sometimes highly heterogeneous, data sources that are often changing …

With crowdsourcing systems, labels can be obtained with low cost, which facilitates the
creation of training sets for prediction model learning. However, the labels obtained from …

Learning with noisy labels is one of the hottest problems in weakly-supervised learning.
Based on memorization effects of deep neural networks, training on small-loss instances …

Image classification systems recently made a giant leap with the advancement of deep
neural networks. However, these systems require an excessive amount of labeled data to be …

Abstract The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object
category classification and detection on hundreds of object categories and millions of …

Crowdsourcing has emerged as a novel problem-solving paradigm, which facilitates
addressing problems that are hard for computers, eg, entity resolution and sentiment …

Saliency in Context (SALICON) is an ongoing effort that aims at understanding and
predicting visual attention. This paper presents a new method to collect large-scale human …

The predictive performance of supervised learning algorithms depends on the quality of
labels. In a typical label collection process, multiple annotators provide subjective noisy …

A longstanding problem in the social, biological, and computational sciences is to determine
how groups of distributed individuals can form intelligent collective judgments. Since …

The large availability of user provided contents on online social media facilitates people
aggregation around shared beliefs, interests, worldviews and narratives. In spite of the …