A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …

Land-cover mapping in remote sensing (RS) applications renders rich information for
decision support and environmental monitoring systems. The derivation of such information …

Cross-modal retrieval methods build a common representation space for samples from
multiple modalities, typically from the vision and the language domains. For images and …

Knowledge of immune cell phenotypes in the tumor microenvironment is essential for
understanding mechanisms of cancer progression and immunotherapy response. We …

Knowledge graph (KG) embedding is to embed components of a KG including entities and
relations into continuous vector spaces, so as to simplify the manipulation while preserving …

A knowledge graph (KG), also known as a knowledge base, is a particular kind of network
structure in which the node indicates entity and the edge represent relation. However, with …

The problem of cross-platform binary code similarity detection aims at detecting whether two
binary functions coming from different platforms are similar or not. It has many security …

Embedding methods have achieved success in face recognition by comparing facial
features in a latent semantic space. However, in a fully unconstrained face setting, the facial …

Kernel classifiers and regressors designed for structured data, such as sequences, trees
and graphs, have significantly advanced a number of interdisciplinary areas such as …

Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics
experiments usually rely on tandem MS to identify the thousands of compounds in a …