[61] Learning to Learn. Springer, 1998. ISBN 978-1-4613-7527-2. doi: 10.1007/978-1-4615-5529-2. URL https://doi.org/10.1007/978-1-4615-5529-2.
[62] Jonathan Baxter. Theoretical models of learning to learn. In Learning to Learn, pages 71-94. Springer, 1998. doi: 10.1007/978-1-4615-5529-2\_4. URL https://doi.org/10.1007/978-1-4615-5529-2_4.
[63] Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel. A simple neural attentive metalearner. In 6th International Conference on Learning Representations. OpenReview.net, 2018. URL https://openreview.net/forum?id=B1DmUzWAW.
[64] Luisa M. Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, and Shimon Whiteson. Fast context adaptation via meta-learning. In Proceedings of the 36th International Conference on Machine Learning, pages 7693-7702. PMLR, 2019. URL http://proceedings.mlr.press/v97/zintgraf19a.html.
[65] 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. In 7th International Conference on Learning Representations. OpenReview.net, 2019. URL https://openreview.net/forum?id=HyztsoC5Y7.
[66] Haozhe Wang, Jiale Zhou, and Xuming He. Learning context-aware task reasoning for efficient meta reinforcement learning. In Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, pages 1440-1448. International Foundation for Autonomous Agents and Multiagent Systems, 2020. URL https://dl.acm.org/doi/abs/10.5555/3398761.3398927.
[67] Richa Upadhyay, Ronald Phlyo, Rajkumar Saini, and Marcus Liwicki. Sharing to learn and learning to share - fitting together meta-learning, multi-task learning, and transfer learning: A meta review. CoRR, abs/2111.12146, 2021. URL https://arxiv.org/abs/2111.12146.
[68] Jacob Beck, Matthew Thomas Jackson, Risto Vuorio, and Shimon Whiteson. Hypernetworks in meta-reinforcement learning. In Conference on Robot Learning, pages 1478-1487. PMLR, 2022. URL https://proceedings.mlr.press/v205/beck23a.html.
[69] Elad Sarafian, Shai Keynan, and Sarit Kraus. Recomposing the reinforcement learning building blocks with hypernetworks. In Proceedings of the 38th International Conference on Machine Learning, pages 9301-9312. PMLR, 2021. URL http://proceedings.mlr.press/v139/sarafian21a.html.
[70] Qinyuan Ye and Xiang Ren. Learning to generate task-specific adapters from task description. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics, pages 646-653. Association for Computational Linguistics, 2021. doi: 10.18653/v1/2021.acl-short.82. URL https://doi.org/10.18653/v1/2021.acl-short.82.
[71] Jonathan Pilault, Amine El hattami, and Christopher Pal. Conditionally adaptive multi-task learning: Improving transfer learning in NLP using fewer parameters & less data. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=de11dbHzAMF.
[72] Rabeeh Karimi Mahabadi, Sebastian Ruder, Mostafa Dehghani, and James Henderson. Parameter-efficient multi-task fine-tuning for transformers via shared hypernetworks. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics, pages 565-576. Association for Computational Linguistics, 2021. doi: 10.18653/v1/2021.acl-long.47. URL https://doi.org/10.18653/v1/2021.acl-long.47.
[73] Hamish Ivison and Matthew E. Peters. Hyperdecoders: Instance-specific decoders for multi-task NLP. In Findings of the Association for Computational Linguistics, pages 1715-1730. Association for Computational Linguistics, 2022. URL https://aclanthology.org/2022.findings-emnlp.124.
[74] Haoxiang Shi, Rongsheng Zhang, Jiaan Wang, Cen Wang, Yinhe Zheng, and Tetsuya Sakai. Layerconnect: Hypernetwork-assisted inter-layer connector to enhance parameter efficiency. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3120-3126. International Committee on Computational Linguistics, 2022. URL https://aclanthology.org/2022.coling-1.276.
[75] Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi. Learning multiple visual domains with residual adapters. In Advances in Neural Information Processing Systems, pages 506-516, 2017. URL https://proceedings.neurips.cc/paper/2017/hash/e7b24b112a44fdd9ee93bdf998c6ca0e-AAbstract.html.