Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

Clustering is a machine learning paradigm of dividing sample subjects into a number of
groups such that subjects in the same groups are more similar to those in other groups. With …

We study the problem of embedding-based entity alignment between knowledge graphs
(KGs). Previous works mainly focus on the relational structure of entities. Some further …

Generating multi-view images from a single-view input is an essential yet challenging
problem. It has broad applications in vision, graphics, and robotics. Our study indicates that …

The data explosion driven by advancements in genomic research, such as high-throughput
sequencing techniques, is constantly challenging conventional methods used in genomics …

The molecular mechanisms and functions in complex biological systems currently remain
elusive. Recent high-throughput techniques, such as next-generation sequencing, have …

Sequential recommendation is a fundamental task for network applications, and it usually
suffers from the item cold start problem due to the insufficiency of user feedbacks. There are …

Advancements in genomic research such as high-throughput sequencing techniques have
driven modern genomic studies into" big data" disciplines. This data explosion is constantly …

With the development of location-based services (LBS), many location-based social sites
like Foursquare and Plancast have emerged. People can organize and participate in group …

Multimodal data fusion (MMDF) is the process of combining disparate data streams (of
different dimensionality, resolution, type, etc.) to generate information in a form that is more …