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Meta-learning is a popular framework for learning with limited data in which an algorithm is produced by training over multiple few-shot learning tasks.
The distribution of information statistics and the criterion of goodness of fit of models
K. Takeuchi · 1976
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Flat minima
S. Hochreiter and J. Schmidhuber · 1997
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Learning to Learn
S. Thrun and L. Pratt · 1998
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Rademacher and gaussian complexities: Risk bounds and structural results
P. L. Bartlett and S. Mendelson · 2002
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One-shot learning of object categories
L. Fei-Fei, R. Fergus, and P. Perona · 2006
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S. Zagoruyko and N. Komodakis · 2011
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On large-batch training for deep learning: Generalization gap and sharp minima
N. S. Keskar, D. Mudigere, J. Nocedal, M. Smelyanskiy, and P. T. P. Tang · 2016
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Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2016
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Matching networks for one shot learning
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra, et al · 2016
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Sharp minima can generalize for deep nets
L. Dinh, R. Pascanu, S. Bengio, and Y. Bengio · 2017
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C. Finn and S. Levine · 2017
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Three factors influencing minima in sgd
S. Jastrzębski, Z. Kenton, D. Arpit, N. Ballas, A. Fischer, Y. Bengio, and A. Storkey · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
J. Snell, K. Swersky, and R. Zemel · 2017
Cited alongside, same era.
Optimization methods for large-scale machine learning
L. Bottou, F. E. Curtis, and J. Nocedal · 2018
Cited alongside, same era.
Probabilistic model-agnostic meta-learning
C. Finn, K. Xu, and S. Levine · 2018
Cited alongside, same era.
Averaging weights leads to wider optima and better generalization
P. Izmailov, D. Podoprikhin, T. Garipov, D. Vetrov, and A. G. Wilson · 2018
Cited alongside, same era.
Visualizing the loss landscape of neural nets
H. Li, Z. Xu, G. Taylor, C. Studer, and T. Goldstein · 2018
A closer look at few-shot classification
W.-Y. Chen, Y.-C. Liu, Z. Kira, Y.-C. F. Wang, and J.-B. Huang · 2019
Later among the works it cites.
Meta-learning with differentiable convex optimization
K. Lee, S. Maji, A. Ravichandran, and S. Soatto · 2019
Later among the works it cites.
Meta-learning with implicit gradients
A. Rajeswaran, C. Finn, S. M. Kakade, and S. Levine · 2019
Later among the works it cites.
Meta-learning without memorization
M. Yin, G. Tucker, M. Zhou, S. Levine, and C. Finn · 2019
Later among the works it cites.
A new meta-baseline for few-shot learning
Y. Chen, X. Wang, Z. Liu, H. Xu, and T. Darrell · 2020
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Cited alongside, same era.
Tadam: Task dependent adaptive metric for improved few-shot learning
B. N. Oreshkin, P. Rodriguez, and A. Lacoste · 2018
Cited alongside, same era.
Meta-learning with latent embedding optimization
A. A. Rusu, D. Rao, J. Sygnowski, O. Vinyals, R. Pascanu, S. Osindero, and R. Hadsell · 2018
Cited alongside, same era.
Learning to compare: Relation network for few-shot learning
F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales · 2018
Cited alongside, same era.
Entropy-sgd: Biasing gradient descent into wide valleys
P. Chaudhari, A. Choromanska, S. Soatto, Y. LeCun, C. Baldassi, C. Borgs, J. Chayes, L. Sagun, and R. Zecchina · 2019
Cited alongside, same era.
Meta-learning with differentiable closed-form solvers
L. Bertinetto, J. F. Henriques, P. H. Torr, and A. Vedaldi
Cited in the paper.
Meta-learning with differentiable closed-form solvers
L. Bertinetto, J. F. Henriques, P. H. S. Torr, and A. Vedaldi
Cited in the paper.
Task-robust model-agnostic meta-learning
L. Collins, A. Mokhtari, and S. Shakkottai · 2020
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Leveraging the feature distribution in transfer-based few-shot learning
Y. Hu, V. Gripon, and S. Pateux · 2020
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Task augmentation by rotating for meta-learning
J. Liu, F. Chao, and C.-M. Lin · 2020
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Charting the right manifold: Manifold mixup for few-shot learning
P. Mangla, N. Kumari, A. Sinha, M. Singh, B. Krishnamurthy, and V. N. Balasubramanian · 2020
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On the interplay between noise and curvature and its effect on optimization and generalization
V. Thomas, F. Pedregosa, B. Merriënboer, P.-A. Manzagol, Y. Bengio, and N. Le Roux · 2020
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