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Unsupervised graph representation learning is critical to a wide range of applications where labels may be scarce or expensive to procure.
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Continual Learning for Fake News Detection from Social Media. In Int. Conf. on Artificial Neural Networks
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Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss. In Proc. Advances in Neural Information Processing Systems (NeurIPS)
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Graph Neural Network for Traffic Forecasting: A Survey
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Self-supervised Graph Neural Networks without explicit negative sampling
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Unsupervised Learning of Visual Features by Contrasting Cluster Assignments. In Proc. Adv. in Neural Information Processing Systems (NeurIPS)
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A Simple Framework for Contrastive Learning of Visual Representations. In Proc. Int. Conf. on Machine Learning (ICML)
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020 · 2020
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Principal Neighbourhood Aggregation for Graph Nets. In Proc. Adv. in Neural Information Processing Systems (NeurIPS)
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Benchmarking Graph Neural Networks
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Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning. In Proc. Adv. in Neural Information Processing Systems (NeurIPS)
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Contrastive Multi-View Representation Learning on Graphs. In Proc. Int. Conf. on Machine Learning (ICML)
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Momentum Contrast for Unsupervised Visual Representation Learning. In Proc. Int. Conf. on Computer Vision and Pattern Recognition (CVPR)
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Strategies for Pre-training Graph Neural Networks. In Proc. Int. Conf. on Learning Representations (ICLR)
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Zekarias T. Kefato and Sarunas Girdzijauskas. 2021 · 2021
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Augmentation-Free Self-Supervised Learning on Graphs
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Graph Self-Supervised Learning: A Survey
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Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning. In Proc. Adv. in Neural Information Processing Systems (NeurIPS)
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Metropolis-Hastings Data Augmentation for Graph Neural Networks. In Proc. Adv. in Neural Information Processing Systems (NeurIPS)
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Can contrastive learning avoid shortcut solutions?. In Proc. Adv. in Neural Information Processing Systems (NeurIPS)
Joshua David Robinson, Li Sun, Ke Yu, kayhan Batmanghelich, Stefanie Jegelka, and Suvrit Sra. 2021 · 2021
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Almost Free Inductive Embeddings Out-Perform Trained Graph Neural Networks in Graph Classification in a Range of Benchmarks
Vadeem Safronov. 2021 · 2021
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CASTing Your Model: Learning to Localize Improves Self-Supervised Representations. In Proc. Int. Conf. on Computer Vision and Pattern Recognition (CVPR)
Ramprasaath R. Selvaraju, Karan Desai, Justin Johnson, and Nikhil Naik. 2021 · 2021
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Adversarial Graph Augmentation to Improve Graph Contrastive Learning. In Proc. Adv. in Neural Information Processing Systems (NeurIPS)
Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville. 2021 · 2021
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Viewmaker Networks: Learning Views for Unsupervised Representation Learning. In Proc. Int. Conf. on Learning Representations (ICLR)
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Bootstrapped Representation Learning on Graphs
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Self-supervised Learning from a Multi-view Perspective. In Proc. Int. Conf. on Learning Representations (ICLR)
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