Specifically, noise regularisation approach corrupts the input graph's nodes with noise, and then adds an autoencoding loss if a node prediction task is not�...
Jun 15, 2021 � In this paper we show that simple noise regularisation can be an effective way to address GNN oversmoothing.
Jun 15, 2021 � Our results show this regularisation method allows the model to monotonically improve in performance with increased message passing steps. Our�...
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This work trains a deep GNN with up to 100 message passing steps and achieves several state-of-the-art results on two challenging molecular property�...
Jun 15, 2021 � Our results show this regularisation method allows the model to monotonically improve in performance with increased message passing steps. Our�...
Abstract. In this paper we show that simple noisy regularisation can be an effective way to address GNN oversmoothing. First we argue that regularisers�...
The main observation of Noisy Nodes is that very deep GNNs can be strongly regularised by appropriate denoising ... Very deep graph neural networks via noise�...
Further, the consistency regularization prevents GNNs from overfitting to noisy labels via mimicry loss in both the inter-view and intra-view perspectives. To�...
Missing: Regularisation. | Show results with:Regularisation.
In this work we present Noisy Nodes, a novel regularisation technique for graph neural networks. ... training strategies for deep graph neural networks. CoRR, abs�...
QM9, Best Noisy Nodes, Test MAE, Mean & Standard Deviation of 3 Seeds. Very Deep Graph Neural Networks Via Noise Regularisation. Preprint. Full-text available.
