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 …

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 …

Quantization based techniques are the current state-of-the-art for scaling maximum inner
product search to massive databases. Traditional approaches to quantization aim to …

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 …

The modern image search system requires semantic understanding of image, and a key yet
under-addressed problem is to learn a good metric for measuring the similarity between …

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 …

Extreme multi-label text classification (XMC) is a task for finding the most relevant labels
from a large label set. Nowadays deep learning-based methods have shown significant …

In many real-world application domains, eg, text categorization and image annotation,
objects naturally belong to more than one class label, giving rise to the multi-label learning …

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 …