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• W2966647818 abstract "Architectures for sparse hierarchical representation learning have recently been proposed for graph-structured data, but so far assume the absence of edge features in the graph. We close this gap and propose a method to pool graphs with edge features, inspired by the hierarchical nature of chemistry. In particular, we introduce two types of pooling layers compatible with an edge-feature graph-convolutional architecture and investigate their performance for molecules relevant to drug discovery on a set of two classification and two regression benchmark datasets of MoleculeNet. We find that our models significantly outperform previous benchmarks on three of the datasets and reach state-of-the-art results on the fourth benchmark, with pooling improving performance for three out of four tasks, keeping performance stable on the fourth task, and generally speeding up the training process." @default.
• W2966647818 created "2019-08-13" @default.
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• W2966647818 date "2019-08-06" @default.
• W2966647818 modified "2023-09-27" @default.
• W2966647818 title "Sparse hierarchical representation learning on molecular graphs" @default.
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