Suggested further readings

Tutorial 2

• Wu, Z., Xiong, Y., Yu, S., & Lin, D. (2018). Unsupervised feature learning via non-parametric instance discrimination (2018)

• Oord, A. van den, Li, Y., & Vinyals, O. (2019). Representation learning with contrastive predictive coding (2018)

• Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations (2020)

• Sohn, K. (2016). Improved Deep Metric Learning with Multi-class N-pair Loss Objective (2016). Advances in Neural Information Processing Systems, 29.

• Gutmann, M., & Hyvärinen, A. (2010). Noise-contrastive estimation: A new estimation principle for unnormalize statistical models (2010). 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)

• Konkle, T., & Alvarez, G. A. (2022). A self-supervised domain-general learning framework for human ventral stream representation (2022). 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). Nature Neuroscience, 21(6), 860–868.