Graph Attention Networks compute learnable attention coefficients over node neighborhoods to produce weighted feature aggregations, achieving state-of-the-art results on citation networks and inductive protein-protein interaction graphs.
org/abs/1611.02344
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abstract
The prevalent approach to neural machine translation relies on bi-directional LSTMs to encode the source sentence. In this paper we present a faster and simpler architecture based on a succession of convolutional layers. This allows to encode the entire source sentence simultaneously compared to recurrent networks for which computation is constrained by temporal dependencies. On WMT'16 English-Romanian translation we achieve competitive accuracy to the state-of-the-art and we outperform several recently published results on the WMT'15 English-German task. Our models obtain almost the same accuracy as a very deep LSTM setup on WMT'14 English-French translation. Our convolutional encoder speeds up CPU decoding by more than two times at the same or higher accuracy as a strong bi-directional LSTM baseline.
representative citing papers
R-Transformer integrates RNNs with multi-head attention to model local and global sequence dependencies without position embeddings and reports large-margin gains over prior methods on diverse tasks.
citing papers explorer
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Graph Attention Networks
Graph Attention Networks compute learnable attention coefficients over node neighborhoods to produce weighted feature aggregations, achieving state-of-the-art results on citation networks and inductive protein-protein interaction graphs.
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R-Transformer: Recurrent Neural Network Enhanced Transformer
R-Transformer integrates RNNs with multi-head attention to model local and global sequence dependencies without position embeddings and reports large-margin gains over prior methods on diverse tasks.