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The constant introduction of standardized benchmarks in the literature has helped accelerating the recent advances in meta-learning research.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al. (1998) · 1998
Earlier work this paper cites.
Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B. (2015) · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · 2015
Earlier work this paper cites.
OpenAI Gym
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W. (2016) · 2016
Earlier work this paper cites.
Meta-Learning with Memory-Augmented Neural Networks
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., and Lillicrap, T. (2016) · 2016
Earlier work this paper cites.
Matching Networks for One Shot Learning
Vinyals, O., Blundell, C., Lillicrap, T. P., Kavukcuoglu, K., and Wierstra, D. (2016) · 2016
Earlier work this paper cites.
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C., Abbeel, P., and Levine, S. (2017) · 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) · 2017
Cited alongside, same era.
Optimization as a model for few-shot learning
Ravi, S. and Larochelle, H. (2017) · 2017
Cited alongside, same era.
Meta-learning with differentiable closed-form solvers
Bertinetto, L., Henriques, J. F., Torr, P. H., and Vedaldi, A. (2018) · 2018
Cited alongside, same era.
Learning to Learn with Gradients
Finn, C. (2018) · 2018
Cited alongside, same era.
Probabilistic model-agnostic meta-learning
Finn, C., Xu, K., and Levine, S. (2018) · 2018
Cited alongside, same era.
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes
Grant, E., Finn, C., Levine, S., Darrell, T., and Griffiths, T. L. (2018) · 2018
Cited alongside, same era.
TADAM: Task dependent adaptive metric for improved few-shot learning
Oreshkin, B., López, P. R., and Lacoste, A. (2018) · 2018
Later among the works it cites.
Meta-learning for semi-supervised few-shot classification
Ren, M., Triantafillou, E., Ravi, S., Snell, J., Swersky, K., Tenenbaum, J. B., Larochelle, H., and Zemel, R. S. (2018) · 2018
Later among the works it cites.
Meta-learning with latent embedding optimization
Rusu, A. A., Rao, D., Sygnowski, J., Vinyals, O., Pascanu, R., Osindero, S., and Hadsell, R. (2018) · 2018
Later among the works it cites.
The Omniglot Challenge: A 3-Year Progress Report
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B. (2019) · 2019
Closest in time.
Meta-Learning with Differentiable Convex Optimization
Lee, K., Maji, S., Ravichandran, A., and Soatto, S. (2019) · 2019
Closest in time.
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Triantafillou, E., Zhu, T., Dumoulin, V., Lamblin, P., Xu, K., Goroshin, R., Gelada, C., Swersky, K., Manzagol, P.-A., and Larochelle, H. (2019) · 2019
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Uncertainty in Multitask Transfer Learning
Lacoste, A., Oreshkin, B., Chung, W., Boquet, T., Rostamzadeh, N., and Krueger, D. (2018) · 2018
Cited alongside, same era.
Closest in time.
Fast Context Adaptation via Meta-Learning
Zintgraf, L. M., Shiarlis, K., Kurin, V., Hofmann, K., and Whiteson, S. (2019) · 2019
Closest in time.