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[1] Andrychowicz, Marcin, Denil, Misha, Gomez, Sergio, Hoffman, Matthew W, Pfau, David, Schaul, Tom, and de Freitas, Nando. Learning to learn by gradient descent by gradient descent. In Advances in Neural Information Processing Systems, pp. 3981–3989, 2016

[2] Ba, Jimmy, Hinton, Geoffrey E, Mnih, Volodymyr, Leibo, Joel Z, and Ionescu, Catalin. Using fast weights to attend to the recent past. In Advances In Neural Information Processing Systems, pp. 4331–4339, 2016

[3] David Ha, Andrew Dai and Le, Quoc V. Hypernetworks. In ICLR 2017, 2017.

[4] Koch, Gregory. Siamese neural networks for one-shot image recognition. PhD thesis, University of Toronto, 2015.

[5] Lake, Brenden M, Salakhutdinov, Ruslan R, and Tenenbaum, Josh. One-shot learning by inverting a compositional causal process. In Advances in neural information processing systems, pp. 2526–2534, 2013.

[6] Santoro, Adam, Bartunov, Sergey, Botvinick, Matthew, Wierstra, Daan, and Lillicrap, Timothy. Meta-learning with memory-augmented neural networks. In Proceedings of The 33rd International Conference on Machine Learning, pp. 1842–1850, 2016.

[7] Vinyals, Oriol, Blundell, Charles, Lillicrap, Tim, Wierstra, Daan, et al. Matching networks for one shot learning. In Advances in Neural Information Processing Systems, pp. 3630–3638, 2016.

[8] Kaiser, Lukasz, Nachum, Ofir, Roy, Aurko, and Bengio, Samy. Learning to remember rare events. In ICLR 2017, 2017.

[9] P. Mirowski, R. Pascanu, F. Viola, H. Soyer, A. Ballard, A. Banino, M. Denil, R. Goroshin, L. Sifre, K. Kavukcuoglu, D. Kumaran, and R. Hadsell. Learning to navigate in complex environments. Techni- cal report, DeepMind, 2016.

[10] B. Zoph and Q. V. Le. Neural architecture search with reinforcement learning. Technical report, submitted to ICLR 2017, 2016.

[11] Y. Duan, J. Schulman, X. Chen, P. Bartlett, I. Sutskever, and P. Abbeel. Rl2: Fast reinforcement learning via slow reinforcement learning. Technical report, UC Berkeley and OpenAI, 2016.

[12] Li, Ke and Malik, Jitendra. Learning to optimize. International Conference on Learning Representations (ICLR), 2017.

[13] Edwards, Harrison and Storkey, Amos. Towards a neural statistician. International Conference on Learning Representations (ICLR), 2017.

[14] Parisotto, Emilio, Ba, Jimmy Lei, and Salakhutdinov, Ruslan. Actor-mimic: Deep multitask and transfer reinforcement learning. International Conference on Learning Representations (ICLR), 2016.

[15] Ravi, Sachin and Larochelle, Hugo. Optimization as a model for few-shot learning. In International Conference on Learning Representations (ICLR), 2017.

[16] Finn, C., Abbeel, P., & Levine, S. (2017). Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. arXiv preprint arXiv:1703.03400.

[17] Chen, Y., Hoffman, M. W., Colmenarejo, S. G., Denil, M., Lillicrap, T. P., & de Freitas, N. (2016). Learning to Learn for Global Optimization of Black Box Functions. arXiv preprint arXiv:1611.03824.

[18] Munkhdalai T, Yu H. Meta Networks. arXiv preprint arXiv:1703.00837, 2017.

[19] Duan Y, Andrychowicz M, Stadie B, et al. One-Shot Imitation Learning. arXiv preprint arXiv:1703.07326, 2017.

[20] Woodward M, Finn C. Active One-shot Learning. arXiv preprint arXiv:1702.06559, 2017.

[21] Wichrowska O, Maheswaranathan N, Hoffman M W, et al. Learned Optimizers that Scale and Generalize. arXiv preprint arXiv:1703.04813, 2017.

[22] Hariharan, Bharath, and Ross Girshick. Low-shot visual object recognition arXiv preprint arXiv:1606.02819 (2016).

[23] Wang J X, Kurth-Nelson Z, Tirumala D, et al. Learning to reinforcement learn. arXiv preprint arXiv:1611.05763, 2016.

[24] Flood Sung, Zhang L, Xiang T, Hospedales T, et al. Learning to Learn: Meta-Critic Networks for Sample Efficient Learning. arXiv preprint arXiv:1706.09529, 2017.

[25] Li Z, Zhou F, Chen F, et al. Meta-SGD: Learning to Learn Quickly for Few Shot Learning. arXiv preprint arXiv:1707.09835, 2017.

[26] Mishra N, Rohaninejad M, Chen X, et al. Meta-Learning with Temporal Convolutions. arXiv preprint arXiv:1707.03141, 2017.

[27] Frans K, Ho J, Chen X, et al. Meta Learning Shared Hierarchies. arXiv preprint arXiv:1710.09767, 2017.

[28] Finn C, Yu T, Zhang T, et al. One-shot visual imitation learning via meta-learning. arXiv preprint arXiv:1709.04905, 2017.

[29] Flood Sung, Yongxin Yang, Zhang Li, Xiang T,Philip Torr, Hospedales T, et al Learning to Compare: Relation Network for Few Shot Learning. arXiv preprint arXiv:1711.06025, 2017.

[30] Brenden M Lake, Ruslan Salakhutdinov, Joshua B Tenenbaum Human-level concept learning through probabilistic program induction. In Science, volume 350, pp. 1332-1338, 2015.

[32] Xu D, Nair S, Zhu Y, et al. Neural task programming: Learning to generalize across hierarchical tasks. arXiv preprint arXiv:1710.01813, 2017.

[33] Bertinetto, L., Henriques, J. F., Valmadre, J., Torr, P., & Vedaldi, A. (2016). Learning feed-forward one-shot learners. In Advances in Neural Information Processing Systems (pp. 523-531).

[34] Wang, Yu-Xiong, and Martial Hebert. Learning to learn: Model regression networks for easy small sample learning. European Conference on Computer Vision. Springer International Publishing, 2016.

[35] Triantafillou, Eleni, Hugo Larochelle, Jake Snell, Josh Tenenbaum, Kevin Jordan Swersky, Mengye Ren, Richard Zemel, and Sachin Ravi. Meta-Learning for Semi-Supervised Few-Shot Classification. ICLR 2018.

[36] Rabinowitz, Neil C., Frank Perbet, H. Francis Song, Chiyuan Zhang, S. M. Eslami, and Matthew Botvinick. Machine Theory of Mind. arXiv preprint arXiv:1802.07740 (2018).

[37] Reed, Scott, Yutian Chen, Thomas Paine, Aäron van den Oord, S. M. Eslami, Danilo Rezende, Oriol Vinyals, and Nando de Freitas. Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions. arXiv preprint arXiv:1710.10304 (2017).

[38] Xu, Zhongwen, Hado van Hasselt, and David Silver. Meta-Gradient Reinforcement Learning arXiv preprint arXiv:1805.09801 (2018).

[39] Xu, Kelvin, Ellis Ratner, Anca Dragan, Sergey Levine, and Chelsea Finn. Learning a Prior over Intent via Meta-Inverse Reinforcement Learning arXiv preprint arXiv:1805.12573 (2018).

[40] Finn, Chelsea, Kelvin Xu, and Sergey Levine. Probabilistic Model-Agnostic Meta-Learning arXiv preprint arXiv:1806.02817 (2018).

[41] Gupta, Abhishek, Benjamin Eysenbach, Chelsea Finn, and Sergey Levine. Unsupervised Meta-Learning for Reinforcement Learning arXiv preprint arXiv:1806.04640(2018).

[42] Yoon, Sung Whan, Jun Seo, and Jaekyun Moon. Meta Learner with Linear Nulling arXiv preprint arXiv:1806.01010 (2018).

[43] Kim, Taesup, Jaesik Yoon, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, and Sungjin Ahn. Bayesian Model-Agnostic Meta-Learning arXiv preprint arXiv:1806.03836 (2018).

[44] Gupta, Abhishek, Russell Mendonca, YuXuan Liu, Pieter Abbeel, and Sergey Levine. Meta-Reinforcement Learning of Structured Exploration Strategies arXiv preprint arXiv:1802.07245 (2018).

[45] Clavera, Ignasi, Anusha Nagabandi, Ronald S. Fearing, Pieter Abbeel, Sergey Levine, and Chelsea Finn. Learning to Adapt: Meta-Learning for Model-Based Control arXiv preprint arXiv:1803.11347 (2018).

[46] Houthooft, Rein, Richard Y. Chen, Phillip Isola, Bradly C. Stadie, Filip Wolski, Jonathan Ho, and Pieter Abbeel. Evolved policy gradients arXiv preprint arXiv:1802.04821 (2018).

[47] Xu, Tianbing, Qiang Liu, Liang Zhao, Wei Xu, and Jian Peng. Learning to Explore with Meta-Policy Gradient arXiv preprint arXiv:1803.05044 (2018).

[48] Stadie, Bradly C., Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, and Ilya Sutskever. Some considerations on learning to explore via meta-reinforcement learning arXiv preprint arXiv:1803.01118 (2018).

[49] Luca Bertinetto, Joao F. Henriques, Philip Torr and Andrea Vedaldi. Meta-learning with differentiable closed-form solvers arXiv preprint arXiv:1805.08136 (2018).

[50] Yoonho Lee, Seungjin Choi. Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace. ICML 2018.