[18] Yao Ming, Panpan Xu, Huamin Qu, and Liu Ren. Interpretable and steerable sequence learning via prototypes. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 903–913, 2019.
[19] Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S Fearing, Pieter Abbeel, Sergey Levine, and Chelsea Finn. Learning to adapt in dynamic, real-world environments through meta-reinforcement learning. arXiv preprint arXiv:1803.11347, 2018.
[20] Samuel Planton, Timo van Kerkoerle, Leila Abbih, Maxime Maheu, Florent Meyniel, Mariano Sigman, Liping Wang, Santiago Figueira, Sergio Romano, and Stanislas Dehaene. A theory of memory for binary sequences: Evidence for a mental compression algorithm in humans. PLoS computational biology, 17(1):e1008598, 2021.
[21] Aravind Rajeswaran, Chelsea Finn, Sham M Kakade, and Sergey Levine. Meta-learning with implicit gradients. Advances in neural information processing systems, 32, 2019.
[22] Aurko Roy, Ashish Vaswani, Arvind Neelakantan, and Niki Parmar. Theory and experiments on vector quantized autoencoders. arXiv preprint arXiv:1805.11063, 2018.
[23] Vivien Sainte Fare Garnot and Loic Landrieu. Metric-guided prototype learning. arXiv preprint arXiv:2007.03047, 2020.
[24] Ravid Shwartz-Ziv and Naftali Tishby. Opening the black box of deep neural networks via information. arXiv preprint arXiv:1703.00810, 2017.
[25] Naftali Tishby and Noga Zaslavsky. Deep learning and the information bottleneck principle. In 2015 IEEE information theory workshop (itw), pages 1-5. IEEE, 2015.
[26] Naftali Tishby, Fernando C Pereira, and William Bialek. The information bottleneck method. arXiv preprint physics/0004057, 2000.
[27] Mycal Tucker, Roger Levy, Julie Shah, and Noga Zaslavsky. Trading off utility, informativeness, and complexity in emergent communication. Neural Information Processing Systems (NeurIPS), 2022.
[28] Mycal Tucker, Roger P. Levy, Julie Shah, and Noga Zaslavsky. Generalization and translatability in emergent communication via informational constraints. In NeurIPS 2022 Workshop on Information-Theoretic Principles in Cognitive Systems, 2022.
[29] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. Advances in neural information processing systems, 30, 2017.
[30] GN Wilkinson and CE Rogers. Symbolic description of factorial models for analysis of variance. Journal of the Royal Statistical Society: Series C (Applied Statistics), 22(3):392-399, 1973.
[31] J Gerard Wolff. Information compression as a unifying principle in human learning, perception, and cognition. Complexity, 2019, 2019.
[32] Hanwei Wu and Markus Flierl. Variational information bottleneck on vector quantized autoencoders. arXiv preprint arXiv:1808.01048, 2018.
[33] Han Xiao, Kashif Rasul, and Roland Vollgraf. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747, 2017.
[34] Justin Yang and Judith Fan. Visual communication of object concepts at different levels of abstraction. Journal of Vision, 21, 2021.
[35] Justin Yang and Judith E Fan. Visual communication of object concepts at different levels of abstraction. arXiv preprint arXiv:2106.02775, 2021.
[36] Noga Zaslavsky, Charles Kemp, Terry Regier, and Naftali Tishby. Efficient compression in color naming and its evolution. Proceedings of the National Academy of Sciences, 115(31): 7937-7942, 2018. doi: 10.1073/pnas.1800521115.