Clustering is a fundamental data exploration technique to discover hidden grouping
structure of data. With the proliferation of big data, and the increase of volume and variety …

We study an Open-World Class Discovery problem in which, given labeled training samples
from old classes, we need to discover new classes from unlabeled test samples. There are …

Real-world datasets inevitably contain biases that arise from different sources or conditions
during data collection. Consequently, such inconsistency itself acts as a confounding factor …

Kernel dimensionality reduction (KDR) algorithms find a low dimensional representation of
the original data by optimizing kernel dependency measures that are capable of capturing …

Kernel dimensionality reduction (KDR) algorithms find a low dimensional representation of
the original data by optimizing kernel dependency measures that are capable of capturing …

Electrical power networks are heavily monitored systems, requiring operators to perform
intricate information synthesis before understanding the underlying network state. Our study …

In this paper, we present a new framework dedicated to the robust detection of
representative variables in high dimensional spaces with a potentially limited number of …

Hierarchical clusterings compactly encode multiple granularities of clusters within a tree
structure. Hierarchies, by definition, fail to capture different flat partitions that are not …

There is currently a debate within the neuroscience community over the likelihood of the
brain performing backpropagation (BP). To better mimic the brain, training a network one …

Powered by the learning capacity of deep neural networks, generative models have
facilitated the scalable modeling of complex, high-dimensional data and are extensively …