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 …

We consider the large-scale query-document retrieval problem: given a query (eg, a
question), return the set of relevant documents (eg, paragraphs containing the answer) from …

Extreme multi-label text classification~(XMC) seeks to find relevant labels from an extreme
large label collection for a given text input. Many real-world applications can be formulated …

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 …

Learned representations are a central component in modern ML systems, serving a
multitude of downstream tasks. When training such representations, it is often the case that …

Scalability and accuracy are well recognized challenges in deep extreme multi-label
learning where the objective is to train architectures for automatically annotating a data point …

Extreme multi-label classification (XML) involves tagging a data point with its most relevant
subset of labels from an extremely large label set, with several applications such as product …

Many large-scale applications amount to finding relevant results from an enormous output
space of potential candidates. For example, finding the best matching product from a large …

Abstract Extreme Multi-label Text Classification (XMTC) is a key task of finding the most
relevant labels from a large label set for a document. Although some deep learning-based …

Multi-label learning deals with training examples each represented by a single instance
while associated with multiple class labels. Due to the exponential number of possible label …