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 tremendously growing problem of phishing e-mail, also known as spam including spear
phishing or spam borne malware, has demanded a need for reliable intelligent anti-spam e …

Sequential fine-tuning and multi-task learning are methods aiming to incorporate knowledge
from multiple tasks; however, they suffer from catastrophic forgetting and difficulties in …

Estimating out-of-distribution (OOD) uncertainty is a major challenge for safely deploying
machine learning models in the open-world environment. Improved methods for OOD …

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 is an important yet challenging task in natural language processing.
It is more complex than single-label classification in that the labels tend to be correlated …

The condition of the vascular network of human eye is an important diagnostic factor in
ophthalmology. Its segmentation in fundus imaging is a nontrivial task due to variable size of …

Predicting all applicable labels for a given image is known as multi-label classification.
Compared to the standard multi-class case (where each image has only one label), it is …

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 …

Clinical coding is the task of transforming medical information in a patient's health records
into structured codes so that they can be used for statistical analysis. This is a cognitive and …