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Jun 15, 2021 · In this paper, we describe a pre-training technique that utilizes large datasets of 3D molecular structures at equilibrium to learn meaningful ...
Jun 15, 2021 · Abstract:In this paper we show that simple noise regularisation can be an effective way to address GNN oversmoothing.
A PyTorch implementation of "Very Deep Graph Neural Networks Via Noise Regularisation" paper, worked as base model of KDD cup 2021 3rd place team Quantum ( ...
From this observation we derive “Noisy Nodes”, a simple technique in which we corrupt the input graph with noise, and add a noise correcting node-level loss.
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 ...
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 ...
While it has been shown that deep models are vulnerable to la- bel noises, robust training of GNNs is generally under-explored, and only a few studies ...
Missing: Regularisation. | Show results with:Regularisation.
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Co-teaching: Robust training of deep neural networks with extremely noisy labels. Advances in Neural Information Processing Systems, Vol. 31 (2018). Google ...
Missing: Regularisation. | Show results with:Regularisation.
Graph neural networks (GNNs) have achieved great success in graph representation learning ... "Very Deep Graph Neural Networks Via Noise Regularisation." arXiv ...