This method maps the original feature space to a new space that balances feature utility, such as classification accuracy, with reducing privacy attack risks, and constructs a kNN graph from this new ...

CellVGAE leverages both the variational graph autoencoder and graph attention networks to offer ... Faiss is only required if using the option --graph_type "KNN Faiss". It is a soft dependency as it ...

Abstract: The K Nearest Neighbor (kNN) is a classification method that's easy to understand ... this paper proposes an unsupervised learning approach that integrates a deep autoencoder for ...

Effectively learning smooth vector representations for graphs of various structures and sizes remains a challenging task. Motivated by the recent advances in deep autoencoders, in this paper, we ...

The feature autoencoder builds and prunes the cell graph through learned embedding. The graph autoencoder adds the graph attention mechanism. It takes the constructed cell graph as input by adding ...

Methods: We developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes ...