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Noisy Labels Can Induce Good Representations
The current success of deep learning depends on large-scale labeled data...
12/23/2020 ∙ by Jingling Li, et al. ∙
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Graph Adversarial Networks: Protecting Information against Adversarial Attacks
We study the problem of protecting information when learning with graph ...
09/28/2020 ∙ by Peiyuan Liao, et al. ∙
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How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
We study how neural networks trained by gradient descent extrapolate, i....
09/24/2020 ∙ by Keyulu Xu, et al. ∙
68
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GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training
Normalization plays an important role in the optimization of deep neural...
09/07/2020 ∙ by Tianle Cai, et al. ∙
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Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization
Cross-lingual word embeddings (CLWE) underlie many multilingual natural ...
06/04/2019 ∙ by Mozhi Zhang, et al. ∙
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What Can Neural Networks Reason About?
Neural networks have successfully been applied to solving reasoning task...
05/30/2019 ∙ by Keyulu Xu, et al. ∙
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Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
While graph kernels (GKs) are easy to train and enjoy provable theoretic...
05/30/2019 ∙ by Simon S. Du, et al. ∙
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How Powerful are Graph Neural Networks?
Graph Neural Networks (GNNs) for representation learning of graphs broad...
10/01/2018 ∙ by Keyulu Xu, et al. ∙
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Representation Learning on Graphs with Jumping Knowledge Networks
Recent deep learning approaches for representation learning on graphs fo...
06/09/2018 ∙ by Keyulu Xu, et al. ∙
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