Recently, numerous studies have been conducted on Missing Value Imputation (MVI),
intending the primary solution scheme for the datasets containing one or more missing …

We tackle the problem of discovering novel classes in an image collection given labelled
examples of other classes. We present a new approach called AutoNovel to address this …

This paper introduces a novel method to perform transfer learning across domains and
tasks, formulating it as a problem of learning to cluster. The key insight is that, in addition to …

Clustering is a well-known unsupervised machine learning approach capable of
automatically grouping discrete sets of instances with similar characteristics. Constrained …

Traditional cluster ensemble approaches have three limitations:() They do not make use of
prior knowledge of the datasets given by experts.() Most of the conventional cluster …

Clustering analysis is widely used as a pre-process to discover the data distribution for
resampling. Existing clustering-based resampling methods mostly run unsupervised …

Seq2seq language generation models that are trained offline with multiple domains in a
sequential fashion often suffer from catastrophic forgetting. Lifelong learning has been …

Transportation of prefabricated modules is a critical process in modular construction and can
cause additional stresses which may damage individual modules, leading to additional …

It is still a challenging problem to characterize uncertainty and imprecision between specific
(singleton) clusters with arbitrary shapes and sizes. In order to solve such a problem, we …

Segmentation of infrared (IR) ship images is always a challenging task, because of the
intensity inhomogeneity and noise. The fuzzy C-means (FCM) clustering is a classical …