# Suggested further readings 

## Tutorial 2

* Wu, Z., Xiong, Y., Yu, S., & Lin, D. (2018). [Unsupervised feature learning via non-parametric instance discrimination (2018)](https://arxiv.org/abs/1805.01978)

* Oord, A. van den, Li, Y., & Vinyals, O. (2019). [Representation learning with contrastive predictive coding (2018)](https://arxiv.org/abs/1807.03748)

* Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). [A simple framework for contrastive learning of visual representations (2020)](https://arxiv.org/abs/2002.05709)

* Sohn, K. (2016). [Improved Deep Metric Learning with Multi-class N-pair Loss Objective (2016)](https://papers.nips.cc/paper_files/paper/2016/hash/6b180037abbebea991d8b1232f8a8ca9-Abstract.html). Advances in Neural Information Processing Systems, 29.

* Gutmann, M., & Hyvärinen, A. (2010). [Noise-contrastive estimation: A new estimation principle for unnormalize statistical models (2010)](https://proceedings.mlr.press/v9/gutmann10a.html). Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 297–304.

* Yeh, C.-H., Hong, C.-Y., Hsu, Y.-C., Liu, T.-L., Chen, Y., & LeCun, Y. (2022). [Decoupled Contrastive Learning (2022)](https://arxiv.org/abs/2110.06848)

* Konkle, T., & Alvarez, G. A. (2022). [A self-supervised domain-general learning framework for human ventral stream representation (2022)](https://www.nature.com/articles/s41467-022-28091-4#Sec9). Nature Communications, 13(1), 491.

* Wang, J. X., Kurth-Nelson, Z., Kumaran, D., Tirumala, D., Soyer, H., Leibo, J. Z., Hassabis, D., & Botvinick, M. (2018). [Prefrontal cortex as a meta-reinforcement learning system (2018)](https://www.nature.com/articles/s41593-018-0147-8). Nature Neuroscience, 21(6), 860–868.