M. Gutmann and A. Hyvärinen. Noise-contrastive estimation: A new estimation princi-
ple for unnormalized statistical models. In Proceedings of the thirteenth international
conference on artiﬁcial intelligence and statistics, pages 297–304. JMLR Workshop and
Conference Proceedings, 2010. 9, 39

R. Hadsell, S. Chopra, and Y. LeCun. Dimensionality reduction by learning an invariant
mapping. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern
Recognition (CVPR’06), volume 2, pages 1735–1742. IEEE, 2006. 7

M. S. Halvagal, A. Laborieux, and F. Zenke. Predictor networks and stop-grads provide
implicit variance regularization in byol/simsiam. arXiv preprint arXiv:2212.04858, 2022.
12

J. Z. HaoChen, C. Wei, A. Gaidon, and T. Ma. Provable guarantees for self-supervised deep
learning with spectral contrastive loss. In Advances in Neural Information Processing
Systems, 2021. 4

T. Hastie, R. Tibshirani, and J. Friedman. Overview of supervised learning. In The elements
A Cookbook of Self-Supervised Learning