Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Aligning distributions of view representations is a core component of today's state of the art
models for deep multi-view clustering. However, we identify several drawbacks with naively …

We present a review on the recent advances and emerging opportunities around the theme
of analyzing deep neural networks (DNNs) with information-theoretic methods. We first …

Current biotechnologies can simultaneously measure multiple high-dimensional modalities
(eg, RNA, DNA accessibility, and protein) from the same cells. A combination of different …

Multi-view clustering has been shown to boost clustering performance by effectively mining
the complementary information from multiple views. However, we observe that learning from …

Self-supervised learning is a central component in recent approaches to deep multi-view
clustering (MVC). However, we find large variations in the development of self-supervision …

Deep multi-view clustering (MVC) is to mine and employ the complex relationships among
views to learn the compact data clusters with deep neural networks in an unsupervised …

Current machine learning models have shown high efficiency in solving a wide variety of
real-world problems. However, their black box character poses a major challenge for the …

With the development of big data, deep learning has made remarkable progress on multi-
view clustering. Multi-view fusion is a crucial technique for the model obtaining a common …

Abstract Recently proposed Graph Neural Networks (GNNs) for vertex clustering are trained
with an unsupervised minimum cut objective, approximated by a Spectral Clustering (SC) …