Multi-label learning studies the problem where each example is represented by a single
instance while associated with a set of labels simultaneously. During the past decade …

Semi-supervised classification methods are suitable tools to tackle training sets with large
amounts of unlabeled data and a small quantity of labeled data. This problem has been …

Partial label learning (PLL), which refers to the classification task where each training
instance is ambiguously annotated with a set of candidate labels, has been recently studied …

Deep convolutional neural networks (DCNNs) trained on a large number of images with
strong pixel-level annotations have recently significantly pushed the state-of-art in semantic …

Partial-label learning (PLL) is a multi-class classification problem, where each training
example is associated with a set of candidate labels. Even though many practical PLL …

Deep ConvNets have shown great performance for single-label image classification (eg
ImageNet), but it is necessary to move beyond the single-label classification task because …

Over the past few decades, Machine Learning (ML) has evolved from the endeavour of few
computer enthusiasts exploiting the possibility of computers learning to play games, and a …

Partial label learning (PLL) is a typical weakly supervised learning problem, where each
training example is associated with a set of candidate labels among which only one is true …

Extracting entities and relations for types of interest from text is important for understanding
massive text corpora. Traditionally, systems of entity relation extraction have relied on …

Partial-label learning (PLL) is a typical weakly supervised learning problem, where each
training instance is equipped with a set of candidate labels among which only one is the true …