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Deep Graph Neural Networks (GNNs) show promising performance on a range of graph tasks, yet at present are costly to run and lack many of the optimisations applied to DNNs.
Fixed-point feedforward deep neural network design using weights + 1 +1 , 0 0 , and − 1 -1
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Image-based recommendations on styles and substitutes
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Focused quantization for sparse cnns
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Probabilistic dual network architecture search on graphs
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