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- Lei Mao’s Log Book, Noise Contrastive Estimation
Balestriero, Randall, Mark Ibrahim, Vlad Sobal, Ari Morcos, Shashank Shekhar, Tom Goldstein, Florian Bordes, et al. 2023. “A Cookbook of Self-Supervised Learning.” arXiv.
Chehab, Omar, Alexandre Gramfort, and Aapo Hyvarinen. 2022. “The Optimal Noise in Noise-Contrastive Learning Is Not What You Think.” arXiv:2203.01110 [Cs, Stat], March.
Gutmann, Michael U., and Aapo Hyvärinen. 2012. “Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics.” Journal of Machine Learning Research 13 (11): 307–61.
Gutmann, Michael, and Aapo Hyvarinen. n.d. “Noise-Contrastive Estimation: A New Estimation Principle for Unnormalized Statistical Models,” 8.
Hinton, Geoffrey E. 2002. “Training Products of Experts by Minimizing Contrastive Divergence.” Neural Computation 14 (8): 1771–1800.
Le-Khac, Phuc H., Graham Healy, and Alan F. Smeaton. 2020. “Contrastive Representation Learning: A Framework and Review.” IEEE Access 8: 193907–34.
Ma, Zhuang, and Michael Collins. 2018. “Noise Contrastive Estimation and Negative Sampling for Conditional Models: Consistency and Statistical Efficiency.” arXiv:1809.01812 [Cs, Stat], September.
Miller, Benjamin Kurt, Christoph Weniger, and Patrick Forré. 2022. “Contrastive Neural Ratio Estimation.” In.
Oord, Aaron van den, Yazhe Li, and Oriol Vinyals. 2019. “Representation Learning with Contrastive Predictive Coding.” arXiv.
Saunshi, Nikunj, Jordan Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham Kakade, and Akshay Krishnamurthy. 2022. “Understanding Contrastive Learning Requires Incorporating Inductive Biases.” arXiv:2202.14037 [Cs], February.
Smith, Noah A., and Jason Eisner. 2005. “Contrastive Estimation: Training Log-Linear Models on Unlabeled Data.” In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL ’05, 354–62. Ann Arbor, Michigan: Association for Computational Linguistics.
Zhang, Yifei, Hao Zhu, Zixing Song, Piotr Koniusz, and Irwin King. 2022. “COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning.” In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2524–34. Washington DC USA: ACM.
Zhu, Hao, Ke Sun, and Piotr Koniusz. 2022. “Contrastive Laplacian Eigenmaps.” arXiv.