Unsupervised Graph Embedding via Adaptive Graph Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"
DOI: 10.1109/tpami.2022.3202158
Abstract: Graph autoencoders (GAEs) are powerful tools in representation learning for graph embedding. However, the performance of GAEs is very dependent on the quality of the graph structure, i.e., of the adjacency matrix. In other words,… read more here.
Keywords: embedding via; unsupervised graph; graph; graph embedding ... See more keywords
Zero-Shot Embedding via Regularization-Based Recollection and Residual Familiarity Processes
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Systems, Man, and Cybernetics: Systems"
DOI: 10.1109/tsmc.2021.3102834
Abstract: The goal of zero-shot learning (ZSL) is to transfer knowledge learned from seen classes during training to unseen classes for testing, with the help of auxiliary information, such as attributes and descriptions. Most of the… read more here.
Keywords: embedding via; seen classes; shot embedding; recollection ... See more keywords
Layout Embedding via Combinatorial Optimization
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DOI: 10.1111/cgf.142632
Abstract: We consider the problem of injectively embedding a given graph connectivity (a layout) into a target surface. Starting from prescribed positions of layout vertices, the task is to embed all layout edges as intersectionâfree paths… read more here.
Keywords: layout embedding; via combinatorial; combinatorial optimization; problem ... See more keywords