Clustering is an essential tool in data mining research and applications. It is the subject of
active research in many fields of study, such as computer science, data science, statistics …

While advances in computing resources have made processing enormous amounts of data
possible, human ability to identify patterns in such data has not scaled accordingly. Efficient …

In this paper, we explore a novel knowledge-transfer task, termed as Deep Model
Reassembly (DeRy), for general-purpose model reuse. Given a collection of heterogeneous …

In recent years, significant advances have been made in the design and evaluation of
balanced (hyper) graph partitioning algorithms. We survey trends of the past decade in …

Recent Advances in Graph Partitioning | SpringerLink Skip to main content Advertisement
SpringerLink Account Menu Find a journal Publish with us Track your research Search Cart …

Wilkinson defined a sparse matrix as one with enough zeros that it pays to take advantage of
them. 1 This informal yet practical definition captures the essence of the goal of direct …

The incredible development of federated learning (FL) has benefited various tasks in the
domains of computer vision and natural language processing, and the existing frameworks …

Clustering is a discovery process in data mining. It groups a set of data in a way that
maximizes the similarity within clusters and minimizes the similarity between two different …

The third edition of this classic tutorial and reference on procedural texturing and modeling
is thoroughly updated to meet the needs of today's 3D graphics professionals and students …

Data cleansing approaches have usually focused on detecting and fixing errors with little
attention to scaling to big datasets. This presents a serious impediment since data cleansing …