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Bayesian meta-learning enables robust and fast adaptation to new tasks with uncertainty assessment.
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Obtaining well calibrated probabilities using bayesian binning
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Variational dropout and the local reparameterization trick
Kingma, D. P., Salimans, T., and Welling, M · 2015
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Cited alongside, same era.
Meta-learning with memory-augmented neural networks
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., and Lillicrap, T · 2016
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Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
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On calibration of modern neural networks
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger · 2017
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Meta-learning with temporal convolutions
N. Mishra, M. Rohaninejad, X. Chen, and P. Abbeel · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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Single-shot refinement neural network for object detection
S. Zhang, L. Wen, X. Bian, Z. Lei, and S. Z. Li · 2018
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Bayesian model-agnostic meta-learning
Yoon, J., Kim, T., Dia, O., Kim, S., Bengio, Y., and Ahn, S · 2018
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Single-shot refinement neural network for object detection
Zhang, S., Wen, L., Bian, X., Lei, Z., and Li, S. Z · 2018
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A closer look at few-shot classification
W.-Y. Chen, Y.-C. Liu, Z. Kira, Y.-C. F. Wang, and J.-B. Huang · 2019
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Meta-learning with implicit gradients
A. Rajeswaran, C. Finn, S. Kakade, and S. Levine · 2019
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Deep leakage from gradients
L. Zhu, Z. Liu, and S. Han · 2019
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