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In this paper, we present a new graph attention neural network, namely GIPA, for attributed graph data learning. GIPA consists of three key components: attention, feature propagation and aggregation. ... The aggregation component uses a residual connection to generate the final embedding. We evaluate the performance of GIPA using the Open Graph Benchmark proteins (ogbn-proteins for short) dataset. ... Wenguang Chen for their support and guidance. ...arXiv:2105.06035v2 fatcat:uscmagd2uzdzdaqjh43bffkh5a
information. ... This approach is based on two ideas: (1) brain-inspired AI, learning human brain architecture to build human-level intelligence, and (2) a probabilistic generative model(PGM)-based cognitive system to ... Scientific Research on Innovative Areas, Brain information dynamics underlying multi-area interconnectivity and parallel processing), and for a project, JPNP16007, subsidized by the New Energy and Industrial ...arXiv:2103.08183v2 fatcat:hn76kje7qng43iohuzncuyo4ba
The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms ... In other words, artificial neural networks and deep learning algorithms have modernized the area. ... Acknowledgements We would like to thank the senior management of Delhi Technological University for their constant support and guidance. ...doi:10.1007/s11030-021-10217-3 pmid:33844136 pmcid:PMC8040371 fatcat:yltthjorrvfrjgyrnszpxgpb2q