[3] Andrea Banino, Jan Balaguer, and Charles Blundell. Pondernet: Learning to ponder. ICML Workshop, abs/2107.05407, 2021. URL https://arxiv.org/abs/2107.05407.
[4] Yoshua Bengio, Nicholas Léonard, and Aaron C. Courville. Estimating or propagating gradients through stochastic neurons for conditional computation. CoRR, abs/1308.3432, 2013. URL http://arxiv.org/abs/1308.3432.
[5] Ben Bogin, Sanjay Subramanian, Matt Gardner, and Jonathan Berant. Latent compositional representations improve systematic generalization in grounded question answering. Transactions of the Association for Computational Linguistics, 9:195-210, 2021. doi: 10.1162/tacl_a_00361. URL https://aclanthology.org/2021.tacl-1.12.
[6] Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 632-642, Lisbon, Portugal, September 2015. Association for Computational Linguistics. doi: 10.18653/v1/D15-1075. URL https://aclanthology.org/D15-1075.
[7] Samuel R. Bowman, Christopher D. Manning, and Christopher Potts. Tree-structured composition in neural networks without tree-structured architectures. In Proceedings of the 2015th International Conference on Cognitive Computation: Integrating Neural and Symbolic Approaches - Volume 1583, COCO'15, page 37-42, Aachen, DEU, 2015. CEUR-WS.org.
[8] Samuel R. Bowman, Jon Gauthier, Abhinav Rastogi, Raghav Gupta, Christopher D. Manning, and Christopher Potts. A fast unified model for parsing and sentence understanding. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1466-1477, Berlin, Germany, August 2016. Association for Computational Linguistics. doi: 10.18653/v1/P16-1139. URL https://aclanthology.org/P16-1139.
[9] Jihun Choi, Kang Min Yoo, and Sang-goo Lee. Learning to compose task-specific tree structures. In Sheila A. McIlraith and Kilian Q. Weinberger, editors, Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, pages 5094-5101. AAAI Press, 2018. URL https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16682.
[10] Jishnu Ray Chowdhury and Cornelia Caragea. Modeling hierarchical structures with continuous recursive neural networks. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 1975-1988. PMLR, 18-24 Jul 2021. URL https://proceedings.mlr.press/v139/chowdhury21a.html.
[11] Caio Corro and Ivan Titov. Learning latent trees with stochastic perturbations and differentiable dynamic programming. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5508-5521, Florence, Italy, July 2019. Association for Computational Linguistics. doi: 10.18653/v1/P19-1551. URL https://aclanthology.org/P19-1551.
[12] Robert Csordás, Kazuki Irie, and Jürgen Schmidhuber. The neural data router: Adaptive control flow in transformers improves systematic generalization. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum?id=KBQP4A_J1K.
[13] Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, and Lukasz Kaiser. Universal transformers. In International Conference on Learning Representations, 2019. URL https://openreview.net/forum?id=HyzdRiR9Y7.
[14] Gr'egoire Del'etang, Anian Ruoss, Jordi Grau-Moya, Tim Genewein, Li Kevin Wenliang, Elliot Catt, Marcus Hutter, Shane Legg, and Pedro A. Ortega. Neural networks and the chomsky hierarchy. ArXiv, abs/2207.02098, 2022.