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 …

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 …

Many recent advancements in Computer Vision are attributed to large datasets. Open-
source software packages for Machine Learning and inexpensive commodity hardware …

The emergence of large-scale genomic, chemical and pharmacological data provides new
opportunities for drug discovery and repositioning. In this work, we develop a computational …

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 …

Time series prediction problems are becoming increasingly high-dimensional in modern
applications, such as climatology and demand forecasting. For example, in the latter …

Abstract Representation learning has shown its effectiveness in many tasks such as image
classification and text mining. Network representation learning aims at learning distributed …

It is well-known that exploiting label correlations is important to multi-label learning. Existing
approaches either assume that the label correlations are global and shared by all instances; …

Visual attributes constitute a large portion of information contained in a scene. Objects can
be described using a wide variety of attributes which portray their visual appearance (color …