Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

Practitioners frequently use diagnostic criteria to identify children with neurodevelopmental
disorders and to guide intervention decisions. These criteria also provide the organising …

We present a novel clustering objective that learns a neural network classifier from scratch,
given only unlabelled data samples. The model discovers clusters that accurately match …

Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …

Detecting anomalies in data is a vital task, with numerous high-impact applications in areas
such as security, finance, health care, and law enforcement. While numerous techniques …

Similarity search is a primitive operation in database and Web search engines. With the
advent of large-scale heterogeneous information networks that consist of multi-typed …

Most objects and data in the real world are of multiple types, interconnected, forming
complex, heterogeneous but often semi-structured information networks. However, most …

Classical collaborative filtering, and content-based filtering methods try to learn a static
recommendation model given training data. These approaches are far from ideal in highly …

Clustering is a widely studied data mining problem in the text domains. The problem finds
numerous applications in customer segmentation, classification, collaborative filtering …

In this paper, we study the linking patterns and discussion topics of political bloggers. Our
aim is to measure the degree of interaction between liberal and conservative blogs, and to …