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People grasp flexible visual concepts from a few examples.
The hungarian method for the assignment problem
Kuhn, H. W · 1955
Earlier work this paper cites.
A density-based algorithm for discovering clusters in large spatial databases with noise
Ester, M., Kriegel, H.-P., Sander, J., and Xu, X · 1996
Earlier work this paper cites.
The big book of concepts
Murphy, G · 2004
Earlier work this paper cites.
Program Synthesis by Sketching
Solar-Lezama, A · 2008
Earlier work this paper cites.
Inverse procedural modeling by automatic generation of l-systems
Stava, O., Benes, B., Mech, R., Aliaga, D. G., and Kristof, P · 2010
Earlier work this paper cites.
Learning structured generative concepts
Stuhlmuller, A., Tenenbaum, J. B., and Goodman, N. D · 2010
Earlier work this paper cites.
Inducing Probabilistic Programs by Bayesian Program Merging
Hwang, I., Stuhlmüller, A., and Goodman, N. D · 2011
Earlier work this paper cites.
Bayesian grammar learning for inverse procedural modeling
Martinovic, A. and Van Gool, L · 2013
Earlier work this paper cites.
Auto-Encoding Variational Bayes
Kingma, D. P. and Welling, M · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Interactive sketching of urban procedural models
Nishida, G., Garcia-Dorado, I., Aliaga, D. G., Benes, B., and Bousseau, A · 2016
Earlier work this paper cites.
One-shot generalization in deep generative models, 2016
Rezende, D. J., Mohamed, S., Danihelka, I., Gregor, K., and Wierstra, D · 2016
Earlier work this paper cites.
Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., kavukcuoglu, k., and Wierstra, D · 2016
Earlier work this paper cites.
Towards a neural statistician
Edwards, H. and Storkey, A · 2017
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Earlier work this paper cites.
Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 2017
Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Du, T., Inala, J. P., Pu, Y., Spielberg, A., Schulz, A., Rus, D., Solar-Lezama, A., and Matusik, W · 2018
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2019
Later among the works it cites.
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Tian, Y., Luo, A., Sun, X., Ellis, K., Freeman, W. T., Tenenbaum, J. B., and Wu, J · 2019
Later among the works it cites.
Learning Generative Models of 3D Structures
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Later among the works it cites.
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Later among the works it cites.
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Ho, J., Jain, A., and Abbeel, P · 2020
Later among the works it cites.
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