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Contrastive Learning (CL) has emerged as a dominant technique for unsupervised representation learning which embeds augmented versions of the anchor close to each other (positive samples) and pushes the embeddings of other samples (negatives) apart.
Mixture models: theory, geometry and applications
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Applications of beta-mixture models in bioinformatics
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Understanding the difficulty of training deep feedforward neural networks
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Distributed representations of words and phrases and their compositionality
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Glove: Global vectors for word representation
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Deepwalk: Online learning of social representations
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An overview of microsoft academic service (mas) and applications
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node2vec: Scalable feature learning for networks
Grover, A. and Leskovec, J · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Representation learning on graphs: Methods and applications
Hamilton, W. L., Ying, R., and Leskovec, J · 2017
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Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2017
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Fastgcn: fast learning with graph convolutional networks via importance sampling
Chen, J., Ma, T., and Xiao, C · 2018
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Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
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Pitfalls of graph neural network evaluation
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Deep metric learning: A survey
Kaya, M. and Bilge, H. Ş · 2019
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Deep graph infomax
Velickovic, P., Fedus, W., Hamilton, W. L., Liò, P., Bengio, Y., and Hjelm, R. D · 2019
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Graphsaint: Graph sampling based inductive learning method
Zeng, H., Zhou, H., Srivastava, A., Kannan, R., and Prasanna, V · 2019
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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Chuang, C.-Y., Robinson, J., Yen-Chen, L., Torralba, A., and Jegelka, S · 2020
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Contrastive multi-view representation learning on graphs
Hassani, K. and Khasahmadi, A. H · 2020
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., and Leskovec, J · 2020
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Mocl: data-driven molecular fingerprint via knowledge-aware contrastive learning from molecular graph
Sun, M., Xing, J., Wang, H., Chen, B., and Zhou, J · 2021
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Adversarial graph augmentation to improve graph contrastive learning
Suresh, S., Li, P., Hao, C., and Neville, J · 2021
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Co-learning: Learning from noisy labels with self-supervision
Tan, C., Xia, J., Wu, L., and Li, S. Z · 2021
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Bootstrapped representation learning on graphs
Thakoor, S., Tallec, C., Azar, M. G., Munos, R., Veličković, P., and Valko, M · 2021
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Directed graph contrastive learning
Tong, Z., Liang, Y., Ding, H., Dai, Y., Li, X., and Wang, C · 2021
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Conditional negative sampling for contrastive learning of visual representations
Wu, M., Mosse, M., Zhuang, C., Yamins, D., and Goodman, N · 2021
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Hard negative mixing for contrastive learning
Kalantidis, Y., Sariyildiz, M. B., Pion, N., Weinzaepfel, P., and Larlus, D · 2020
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Wiki-cs: A wikipedia-based benchmark for graph neural networks
Mernyei, P. and Cangea, C · 2020
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Graph representation learning via graphical mutual information maximization
Peng, Z., Huang, W., Luo, M., Zheng, Q., Rong, Y., Xu, T., and Huang, J · 2020
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Gcc: Graph contrastive coding for graph neural network pre-training
Qiu, J., Chen, Q., Dong, Y., Zhang, J., Yang, H., Ding, M., Wang, K., and Tang, J · 2020
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Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization
Sun, F.-Y., Hoffman, J., Verma, V., and Tang, J · 2020
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Graph contrastive learning with augmentations
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., and Shen, Y · 2020
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Deep graph contrastive representation learning
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., and Wang, L · 2020
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Towards robust graph neural networks against label noise, 2021
Xia, J., Lin, H., Xu, Y., Wu, L., Gao, Z., Li, S., and Li, S. Z · 2021
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InfoGCL: Information-aware graph contrastive learning
Xu, D., Cheng, W., Luo, D., Chen, H., and Zhang, X · 2021
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Graph contrastive learning automated
You, Y., Chen, T., Shen, Y., and Wang, Z · 2021
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From canonical correlation analysis to self-supervised graph neural networks
Zhang, H., Wu, Q., Yan, J., Wipf, D., and Philip, S. Y · 2021
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Graph debiased contrastive learning with joint representation clustering
Zhao, H., Yang, X., Wang, Z., Yang, E., and Deng, C · 2021
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Ot cleaner: Label correction as optimal transport
Xia, J., Tan, C., Wu, L., Xu, Y., and Li, S. Z · 2022
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Simgrace: A simple framework for graph contrastive learning without data augmentation
Xia, J., Wu, L., Chen, J., Hu, B., and Li, S. Z · 2022
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Bringing your own view: Graph contrastive learning without prefabricated data augmentations, 2022
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Using context-to-vector with graph retrofitting to improve word embeddings
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Structure-Enhanced Heterogeneous Graph Contrastive Learning
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Graph contrastive learning with adaptive augmentation
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., and Wang, L · 2080
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