This work presents a two-stage neural architecture for learning and refining structural
correspondences between graphs. First, we use localized node embeddings computed by a …

Code retrieval is to find the code snippet from a large corpus of source code repositories that
highly matches the query of natural language description. Recent work mainly uses natural …

Recent years have shown great merits in utilizing neuroimaging data to understand brain
structural and functional changes, as well as its relationship to different neurodegenerative …

This survey gives a selective review of recent development of machine learning (ML) for
combinatorial optimization (CO), especially for graph matching. The synergy of these two …

Combinatorial optimization (CO) on the graph is a crucial but challenging research topic.
Recent quantum algorithms provide a new perspective for solving CO problems and have …

Answering semantically complicated questions according to an image is challenging in a
visual question answering (VQA) task. Although the image can be well represented by deep …

Graph matching is a commonly used technique in computer vision and pattern recognition.
Recent data-driven approaches have improved the graph matching accuracy remarkably …

In this paper, we study the problem of speeding up a type of optimization algorithms called
Frank-Wolfe, a conditional gradient method. We develop and employ two novel inner …

This paper proposes a novel topological learning framework that integrates networks of
different sizes and topology through persistent homology. Such challenging task is made …

Graph matching (GM) which is the problem of finding vertex correspondence among two or
multiple graphs is a fundamental problem in computer vision and pattern recognition. GM …