Extreme multi-label text classification (XMTC) refers to the problem of assigning to each
document its most relevant subset of class labels from an extremely large label collection …

Multi-label classification has gained in importance in the last decade and it is today
confronted to the current needs to process massive raw data from heterogeneous sources …

In this paper, we propose a novel deep learning architecture for multi-label zero-shot
learning (ML-ZSL), which is able to predict multiple unseen class labels for each input …

The objective in extreme multi-label learning is to train a classifier that can automatically tag
a novel data point with the most relevant subset of labels from an extremely large label set …

The choice of the loss function is critical in extreme multi-label learning where the objective
is to annotate each data point with the most relevant subset of labels from an extremely large …

Multi-label classification is a practical yet challenging task in machine learning related fields,
since it requires the prediction of more than one label category for each input instance. We …

The objective in extreme multi-label classification is to learn a classifier that can
automatically tag a data point with the most relevant subset of labels from a large label set …

Obstacles hindering the development of capsule networks for challenging NLP applications
include poor scalability to large output spaces and less reliable routing processes. In this …

In many practical applications of supervised learning the task involves the prediction of
multiple target variables from a common set of input variables. When the prediction targets …

Information theoretical-based methods have attracted a great attention in recent years and
gained promising results for multilabel feature selection (MLFS). Nevertheless, most of the …