The requirement for appropriate ways to measure the similarity between data objects is a
common but vital task in various domains, such as data mining, machine learning and so on …

Node classification is a popular and challenging task in graph neural networks, and existing
approaches are mainly developed for a single network. With the advances in domain …

We propose a simple yet effective metric that measures structural similarity between visual
instances of architectural floor plans, without the need for learning. Qualitatively, our …

The power grid is going through significant changes with the introduction of renewable
energy sources and the incorporation of smart grid technologies. These rapid advancements …

Substitution matrices, which are crafted to quantify the functional impact of substitutions or
deletions in biomolecules, are central component of remote homology detection, functional …

Cyber-Physical Production Systems (CPPSs) are highly variable systems of systems
comprised of software and hardware interacting with each other and the environment. The …

Recent advances in fine-grained representation learning leverage local-to-global
(emergent) relationships for achieving state-of-the-art results. The relational representations …

Keyword spotting (KWS) in historical documents is an important tool for the initial exploration
of digitized collections. Nowadays, the most efficient KWS methods are relying on machine …

We consider the problem of program clone search, ie given a target program and a
repository of known programs (all in executable format), the goal is to find the program in the …

Distance measures between graphs are important primitives for a variety of learning tasks. In
this work, we describe an unsupervised, optimal transport based approach to define a …