Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …

The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …

In a typical multi-label setting, a picture contains on average few positive labels, and many
negative ones. This positive-negative imbalance dominates the optimization process, and …

Multi-label image classification is the task of predicting a set of labels corresponding to
objects, attributes or other entities present in an image. In this work we propose the …

In a typical multi-label setting, a picture contains on average few positive labels, and many
negative ones. This positive-negative imbalance dominates the optimization process, and …

Extreme multi-label text classification (XMTC) is an important problem in the era of {\it big
data}, for tagging a given text with the most relevant multiple labels from an extremely large …

We consider the extreme multi-label text classification (XMC) problem: given an input text,
return the most relevant labels from a large label collection. For example, the input text could …

Abstract Multi-Label Text Classification (MLTC) is a fundamental and challenging task in
natural language processing. Previous studies mainly focus on learning text representation …

People enjoy food photography because they appreciate food. Behind each meal there is a
story described in a complex recipe and, unfortunately, by simply looking at a food image we …

Multi-label text classification is a challenging task because it requires capturing label
dependencies. It becomes even more challenging when class distribution is long-tailed …