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Compared to humans, machine learning models generally require significantly more training examples and fail to extrapolate from experience to solve previously unseen challenges.
Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
Schmidhuber, J · 1987
Earlier work this paper cites.
On the optimization of a synaptic learning rule
Bengio, S., Bengio, Y., Cloutier, J., and Gecsei, J · 1992
Earlier work this paper cites.
Meta-neural networks that learn by learning
Naik, D. K. and Mammone, R. J · 1992
Earlier work this paper cites.
A hypercube-based encoding for evolving large-scale neural networks
Stanley, K. O., D’Ambrosio, D. B., and Gauci, J · 2009
Earlier work this paper cites.
Learning to learn
Thrun, S. and Pratt, L · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Siamese neural networks for one-shot image recognition
Koch, G., Zemel, R., and Salakhutdinov, R · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al · 2015
Earlier work this paper cites.
Unsupervised learning of video representations using lstms
Srivastava, N., Mansimov, E., and Salakhutdinov, R · 2015
Earlier work this paper cites.
Learning to learn by gradient descent by gradient descent
Andrychowicz, M., Denil, M., Gomez, S., Hoffman, M. W., Pfau, D., Schaul, T., Shillingford, B., and De Freitas, N · 2016
Cited alongside, same era.
A compositional object-based approach to learning physical dynamics
Chang, M. B., Ullman, T., Torralba, A., and Tenenbaum, J. B · 2016
Cited alongside, same era.
Rl2: Fast reinforcement learning via slow reinforcement learning
Duan, Y., Schulman, J., Chen, X., Bartlett, P. L., Sutskever, I., and Abbeel, P · 2016
Cited alongside, same era.
Edwards, H. and Storkey, A · 2016
Cited alongside, same era.
Convolution by evolution: Differentiable pattern producing networks
Fernando, C., Banarse, D., Reynolds, M., Besse, F., Pfau, D., Jaderberg, M., Lanctot, M., and Wierstra, D · 2016
Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., kavukcuoglu, k., and Wierstra, D · 2016
Later among the works it cites.
Learning to reinforcement learn
Wang, J. X., Kurth-Nelson, Z., Tirumala, D., Soyer, H., Leibo, J. Z., Munos, R., Blundell, C., Kumaran, D., and Botvinick, M · 2016
Later among the works it cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Later among the works it cites.
A disentangled recognition and nonlinear dynamics model for unsupervised learning
Fraccaro, M., Kamronn, S., Paquet, U., and Winther, O · 2017
Later among the works it cites.
Schema networks: Zero-shot transfer with a generative causal model of intuitive physics
Kansky, K., Silver, T., Mély, D. A., Eldawy, M., Lázaro-Gredilla, M., Lou, X., Dorfman, N., Sidor, S., Phoenix, D. S., and George, D · 2017
Later among the works it cites.
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Cited alongside, same era.
Ha, D., Dai, A., and Le, Q. V · 2016
Cited alongside, same era.
Evolvability search:directly selecting for evolvability in order to study and produce it
Mengistu, H., Lehman, J., and Clune, J · 2016
Cited alongside, same era.
Optimization as a model for few-shot learning
Ravi, S. and Larochelle, H · 2016
Cited alongside, same era.
Meta-learning with memory-augmented neural networks
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., and Lillicrap, T · 2016
Cited alongside, same era.
Li, Z., Zhou, F., Chen, F., and Li, H · 2017
Later among the works it cites.
Visual interaction networks: Learning a physics simulator from video
Watters, N., Zoran, D., Weber, T., Battaglia, P., Pascanu, R., and Tacchetti, A · 2017
Later among the works it cites.
Learning to see physics via visual de-animation
Wu, J., Lu, E., Kohli, P., Freeman, B., and Tenenbaum, J · 2017
Later among the works it cites.