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Found 3 papers, 1 papers with code
State of the Art and Potentialities of Graph-level Learning
Traditional approaches to learning a set of graphs heavily rely on hand-crafted features, such as substructures.
Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach
The architecture is based on a novel mapping from real-world data to Hilbert space.
Region-wise Generative Adversarial ImageInpainting for Large Missing Areas
To address these problems, we propose a generic inpainting framework capable of handling with incomplete images on both continuous and discontinuous large missing areas, in an adversarial manner.
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