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Three years ago, we released the Omniglot dataset for one-shot learning, along with five challenge tasks and a computational model that addresses these tasks.
A study of thinking
Bruner, J. S., Goodnow, J. J., and Austin, G. A. (1956) · 1956
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Learning and memorization of classifications
Shepard, R. N., Hovland, C. L., and Jenkins, H. M. (1961) · 1961
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Metamagical themas: Questing for the essence of mind and pattern
Hofstadter, D. R. (1985) · 1985
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The Big Book of Concepts
Murphy, G. L. (2002) · 2002
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Object name learning provides on-the-job training for attention
Smith, L. B., Jones, S. S., Landau, B., Gershkoff-Stowe, L., and Samuelson, L. (2002) · 2002
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Inferring motor programs from images of handwritten digits
Hinton, G. E. and Nair, V. (2006) · 2006
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ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009) · 2009
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012) · 2012
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Concept learning as motor program induction: A large-scale empirical study
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B. (2012) · 2012
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The arcade learning environment: An evaluation platform for general agents
Bellemare, M. G., Naddaf, Y., Veness, J., and Bowling, M. (2013) · 2013
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DRAW: A Recurrent Neural Network For Image Generation
Gregor, K., Danihelka, I., Graves, A., Rezende, D. J., and Wierstra, D. (2015) · 2015
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Siamese neural networks for one-shot image recognition
Koch, G., Zemel, R. S., and Salakhutdinov, R. (2015) · 2015
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Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B. (2015) · 2015
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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., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., and Hassabis, D. (2015) · 2015
Cited alongside, same era.
Towards a Neural Statistician
Edwards, H. and Storkey, A. (2016) · 2016
Cited alongside, same era.
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
Eslami, S. M. A., Heess, N., Weber, T., Tassa, Y., Kavukcuoglu, K., and Hinton, G. E. (2016) · 2016
Cited alongside, same era.
Towards Conceptual Compression
Gregor, K., Besse, F., Rezende, D. J., Danihelka, I., and Wierstra, D. (2016) · 2016
Cited alongside, same era.
Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes
Causal generative models are just a start
Davis, E. and Marcus, G. (2017) · 2017
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Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C., Abbeel, P., and Levine, S. (2017) · 2017
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A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs
George, D., Lehrach, W., Kansky, K., Laan, C., Marthi, B., Lou, X., Meng, Z., Liu, Y., Wang, H., Lavin, A., and Phoenix, D. S. (2017) · 2017
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Building on prior knowledge without building it in
Hansen, S. S., Lampinen, A. K., Suri, G., and McClelland, J. L. (2017) · 2017
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Attentive recurrent comparators
Shyam, P., Gupta, S., and Dukkipati, A. (2017) · 2017
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Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R. S. (2017) · 2017
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Rae, J. W., Hunt, J. J., Harley, T., Danihelka, I., Senior, A., Wayne, G., Graves, A., and Lillicrap, T. P. (2016) · 2016
Cited alongside, same era.
One-Shot Generalization in Deep Generative Models
Rezende, D. J., Mohamed, S., Danihelka, I., Gregor, K., and Wierstra, D. (2016) · 2016
Cited alongside, same era.
Meta-Learning with Memory-Augmented Neural Networks
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., and Lillicrap, T. (2016) · 2016
Cited alongside, same era.
Matching Networks for One Shot Learning
Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., and Wierstra, D. (2016) · 2016
Cited alongside, same era.
Active One-shot Learning
Woodward, M. and Finn, C. (2016) · 2016
Cited alongside, same era.
Building Machines that Learn and Think for Themselves
Botvinick, M., Barrett, D., Battaglia, P., de Freitas, N., Kumaran, D., Leibo, J. Z., Lillicrap, T., Modayil, J., Mohamed, S., Rabinowitz, N., Rezende, D. J., Santoro, A., Schaul, T., Summerfield, C., Wayne, G., Weber, T., Wierstra, D., Legg, S., and Hassabis, D. (2017) · 2017
Cited alongside, same era.
Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J. (2017a)
Cited in the paper.
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Synthesizing Programs for Images using Reinforced Adversarial Learning
Ganin, Y., Kulkarni, T., Babuschkin, I., Eslami, S. M. A., and Vinyals, O. (2018) · 2018
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Few-shot learning with graph neural networks
Garcia, V. and Bruna, J. (2018) · 2018
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The Variational Homoencoder: Learning to learn high capacity generative models from few examples
Hewitt, L. B., Nye, M. I., Gane, A., Jaakkola, T., and Tenenbaum, J. B. (2018) · 2018
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Artificial Intelligence Hits the Barrier of Meaning
Mitchell, M. (2018) · 2018
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personal communication
Shyam, P. (2018) · 2018
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